prediction markets

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Infotopia: How Many Minds Produce Knowledge by Cass R. Sunstein

affirmative action, Andrei Shleifer, availability heuristic, behavioural economics, Build a better mousetrap, c2.com, Cass Sunstein, cognitive bias, cuban missile crisis, Daniel Kahneman / Amos Tversky, Edward Glaeser, en.wikipedia.org, feminist movement, framing effect, Free Software Foundation, hindsight bias, information asymmetry, Isaac Newton, Jean Tirole, jimmy wales, market bubble, market design, minimum wage unemployment, prediction markets, profit motive, rent control, Richard Stallman, Richard Thaler, Robert Shiller, Ronald Reagan, Savings and loan crisis, slashdot, stem cell, systematic bias, Ted Sorensen, the Cathedral and the Bazaar, The Wisdom of Crowds, winner-take-all economy

If the question is the likely winner of an Oscar, or the probability of a natural disaster, or the outcome of an election, there is every reason to pay a great deal of attention to prediction markets. I have suggested that in many domains, private and public institutions should consider the use of such markets to supplement deliberative processes. Government agencies, including those involved with national security (such as the Department of Defense and the Central Intelligence Agency), should experiment with internal prediction markets. To be sure, we do not yet know exactly when prediction markets will work. (Perhaps a prediction market could tell us; shall we bet?) We do know that when people lack information to aggregate, prediction markets are not particularly helpful.

Recall that under the Condorcet Jury Theorem, the average vote of a large group will be wrong if most group members are likely to err. In a prediction market, the existence of incentives greatly increases the likelihood that each investor will prove to be right. Those without information will not participate; those with a lot of information will Money, Prices, and Prediction Markets / 105 participate a great deal. Crucially, the problems that infect deliberating groups are reduced in prediction markets. As a result, such markets have often proved remarkably accurate. Perhaps most important, prediction markets have been found not to amplify individual errors but to eliminate them; the prices that result from trading prove reliable even if many individual traders err.

An interesting counterpoint: Tradesports.com, a prediction market that uses real rather than virtual money, predicted only six of eight Oscar winners in that year. In fact, prediction markets that use virtual money have been found, in many circumstances, to do as well as markets that rely on real money.23 When entertainment and law meet, prediction markets do well. Days before the ultimate verdict in the Michael Jackson case, insiders knew what would happen. As one reporter noted in advance, “Whether or not Michael Jackson’s jurors still have a reasonable doubt about his guilt, Money, Prices, and Prediction Markets / 111 the wild world of Internet betting has rendered judgment: the smart money is on acquittal.”24 Many people believe that “you can’t predict the weather,” but the National Weather Service does quite well, and orange juice futures do even better.25 The markets for the demand for gas outperform the experts on the demand for gas.26 A large prediction market focuses on the likelihood that economic data released later in the week will show specific values;27 the market has performed at least as well as the consensus forecasts of a survey of about fifty professional forecasters.


pages: 271 words: 52,814

Blockchain: Blueprint for a New Economy by Melanie Swan

23andMe, Airbnb, altcoin, Amazon Web Services, asset allocation, banking crisis, basic income, bioinformatics, bitcoin, blockchain, capital controls, cellular automata, central bank independence, clean water, cloud computing, collaborative editing, Conway's Game of Life, crowdsourcing, cryptocurrency, data science, digital divide, disintermediation, Dogecoin, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, financial innovation, Firefox, friendly AI, Hernando de Soto, information security, intangible asset, Internet Archive, Internet of things, Khan Academy, Kickstarter, Large Hadron Collider, lifelogging, litecoin, Lyft, M-Pesa, microbiome, Neal Stephenson, Network effects, new economy, operational security, peer-to-peer, peer-to-peer lending, peer-to-peer model, personalized medicine, post scarcity, power law, prediction markets, QR code, ride hailing / ride sharing, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Skype, smart cities, smart contracts, smart grid, Snow Crash, software as a service, synthetic biology, technological singularity, the long tail, Turing complete, uber lyft, unbanked and underbanked, underbanked, Vitalik Buterin, Wayback Machine, web application, WikiLeaks

A better way to crowdfund cryptocurrency projects in a decentralized yet legal way, with more effective checks and balances, is needed. Bitcoin Prediction Markets One example of new tech with old tech is Bitcoin prediction markets like Predictious and Fairlay.50 Bitcoin prediction markets offer a betting venue for the usual real-world outcomes as prediction markets always have, such as elections, political legislation, sports matches, and technology product releases, and also serve as a good source of information about the developing blockchain industry. Bitcoin prediction markets are one way to see what insiders think about Bitcoin’s future price directions, the success of different altcoin and protocol 2.0 projects, and industry issues more generally (e.g., technical development issues with Bitcoin, such as when there will be a hard fork—significant change—of the code, and the level of difficulty of the mining algorithm).

-M2M/IoT Bitcoin Payment Network to Enable the Machine Economy and consensus models, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe extensibility of, Extensibility of Blockchain Technology Concepts for facilitating big data predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation future applications, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe limitations of (see limitations) organizational capabilities, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity tracking capabilities, Fundamental Economic Principles: Discovery, Value Attribution, and Exchange-Fundamental Economic Principles: Discovery, Value Attribution, and Exchange blockchain-recorded marriage, Decentralized Governance Services BlockCypher, Blockchain Development Platforms and APIs BOINC, DAOs and DACs bond deposit postings, Technical Challenges Brin, David, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel BTCjam, Financial Services business model challenges, Business Model Challenges Buttercoin, Financial Services Byrne, Patrick, Financial Services C Campus Cryptocurrency Network, Campuscoin Campuscoin, Campuscoin-Campuscoin censorship, Internet (see decentralized DNS system) Chain, Blockchain Development Platforms and APIs challenges (see see limitations) charity donations, Charity Donations and the Blockchain—Sean’s Outpost China, Relation to Fiat Currency ChromaWallet, Wallet Development Projects Chronobit, Virtual Notary, Bitnotar, and Chronobit Circle Internet Financial, eWallet Services and Personal Cryptosecurity Codius, Financial Services coin drops, Coin Drops as a Strategy for Public Adoption coin mixing, eWallet Services and Personal Cryptosecurity coin, defining, Terminology and Concepts, Currency, Token, Tokenizing Coinapult, Global Public Health: Bitcoin for Contagious Disease Relief Coinapult LOCKS, Relation to Fiat Currency Coinbase, Merchant Acceptance of Bitcoin, Financial Services CoinBeyond, Merchant Acceptance of Bitcoin Coinffeine, Financial Services Coinify, Merchant Acceptance of Bitcoin Coinprism, Wallet Development Projects Coinspace, Crowdfunding CoinSpark, Wallet Development Projects colored coins, Smart Property, Blockchain 2.0 Protocol Projects community supercomputing, Community Supercomputing Communitycoin, Currency, Token, Tokenizing-Communitycoin: Hayek’s Private Currencies Vie for Attention complementary currency systems, Demurrage Currencies: Potentially Incitory and Redistributable concepts, redefining, Terminology and Concepts-Terminology and Concepts consensus models, Blockchain AI: Consensus as the Mechanism to Foster “Friendly” AI-Blockchain Consensus Increases the Information Resolution of the Universe consensus-derived information, Blockchain Consensus Increases the Information Resolution of the Universe contagious disease relief, Global Public Health: Bitcoin for Contagious Disease Relief contracts, Blockchain 2.0: Contracts-The Blockchain as a Path to Artificial Intelligence (see also smart contracts) crowdfunding, Crowdfunding-Crowdfunding financial services, Financial Services-Financial Services marriage, Decentralized Governance Services prediction markets, Bitcoin Prediction Markets smart property, Smart Property-Smart Property wallet development projects, Wallet Development Projects copyright protection, Monegraph: Online Graphics Protection Counterparty, Blockchain 2.0 Protocol Projects, Counterparty Re-creates Ethereum’s Smart Contract Platform Counterparty currency (XCP), Currency, Token, Tokenizing Counterwallet, Wallet Development Projects crowdfunding, Crowdfunding-Crowdfunding cryptocurrencies benefits of, Currency, Token, Tokenizing cryptosecurity, eWallet Services and Personal Cryptosecurity eWallet services, eWallet Services and Personal Cryptosecurity mechanics of, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin merchant acceptance, Merchant Acceptance of Bitcoin cryptosecurity challenges, eWallet Services and Personal Cryptosecurity cryptowallet, Blockchain Neutrality currency, Technology Stack: Blockchain, Protocol, Currency-Regulatory Status, Currency, Token, Tokenizing-Extensibility of Demurrage Concept and Features Campuscoin, Campuscoin-Campuscoin coin drops, Coin Drops as a Strategy for Public Adoption Communitycoin, Communitycoin: Hayek’s Private Currencies Vie for Attention-Communitycoin: Hayek’s Private Currencies Vie for Attention cryptocurrencies, How a Cryptocurrency Works-Merchant Acceptance of Bitcoin decentralizing, Communitycoin: Hayek’s Private Currencies Vie for Attention defining, Currency, Token, Tokenizing-Currency, Token, Tokenizing, Currency: New Meanings demurrage, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems fiat currency, Relation to Fiat Currency-Relation to Fiat Currency monetary and nonmonetary, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies new meanings, Currency: New Meanings technology stack, Technology Stack: Blockchain, Protocol, Currency-Technology Stack: Blockchain, Protocol, Currency currency mulitplicity, Currency Multiplicity: Monetary and Nonmonetary Currencies-Currency Multiplicity: Monetary and Nonmonetary Currencies D DAOs, DAOs and DACs-DAOs and DACs DAOs/DACs, DAOs and DACs-DAOs and DACs, Batched Notary Chains as a Class of Blockchain Infrastructure, Blockchain Government Dapps, Dapps-Dapps, Extensibility of Demurrage Concept and Features Dark Coin, eWallet Services and Personal Cryptosecurity dark pools, Technical Challenges Dark Wallet, eWallet Services and Personal Cryptosecurity DASs, DASs and Self-Bootstrapped Organizations DDP, Crowdfunding decentralization, Smart Contracts, Centralization-Decentralization Tension and Equilibrium decentralized applications (Dapps), Dapps-Dapps decentralized autonomous organization/corporation (DAO) (see DAOs/DACs) decentralized autonomous societies (DASs), DASs and Self-Bootstrapped Organizations decentralized autonomy, eWallet Services and Personal Cryptosecurity decentralized DNS, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity challenges of, Challenges and Other Decentralized DNS Services and digital identity, Decentralized DNS Functionality Beyond Free Speech: Digital Identity-Decentralized DNS Functionality Beyond Free Speech: Digital Identity DotP2P, Challenges and Other Decentralized DNS Services decentralized file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation decentralized secure file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation deeds, Decentralized Governance Services demurrage currencies, Demurrage Currencies: Potentially Incitory and Redistributable-Extensibility of Demurrage Concept and Features action-incitory features, Extensibility of Demurrage Concept and Features limitations of, Demurrage Currencies: Potentially Incitory and Redistributable digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also blockchain attestation services) hashing and timestamping, Hashing Plus Timestamping-Limitations online graphics protection, Monegraph: Online Graphics Protection digital cryptography, Ethereum: Turing-Complete Virtual Machine, Public/Private-Key Cryptography 101 digital divide, defining, Digital Divide of Bitcoin digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records dispute resolution, PrecedentCoin: Blockchain Dispute Resolution DIYweathermodeling, Community Supercomputing DNAnexus, Genomecoin, GenomicResearchcoin Dogecoin, Technology Stack: Blockchain, Protocol, Currency, Currency Multiplicity: Monetary and Nonmonetary Currencies, Scandals and Public Perception DotP2P, Challenges and Other Decentralized DNS Services double-spend problem, The Double-Spend and Byzantine Generals’ Computing Problems DriveShare, DAOs and DACs dynamic redistribution of currency (see demurrage currency) E education (see learning and literacy) Electronic Freedom Foundation (EFF), Distributed Censorship-Resistant Organizational Models EMR (electronic medical record) system, EMRs on the Blockchain: Personal Health Record Storage Ethereum, Crowdfunding, Blockchain 2.0 Protocol Projects, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine-Counterparty Re-creates Ethereum’s Smart Contract Platform eWallet services, eWallet Services and Personal Cryptosecurity ExperimentalResultscoin, Blockchain Academic Publishing: Journalcoin F Fairlay, Bitcoin Prediction Markets fiat currency, Relation to Fiat Currency-Relation to Fiat Currency file serving, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Ethereum: Turing-Complete Virtual Machine file storage, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation financial services, Regulatory Status, Financial Services-Financial Services, Blockchain Technology Is a New and Highly Effective Model for Organizing Activity, Government Regulation Fitbit, Personal Thinking Blockchains, Blockchain Health Research Commons, Extensibility of Demurrage Concept and Features Florincoin, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel Folding@Home, DAOs and DACs, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing franculates, Blockchain Government freedom of speech, Namecoin: Decentralized Domain Name System, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel (see also decentralized DNS system) Freicoin, Demurrage Currencies: Potentially Incitory and Redistributable fundraising (see crowdfunding) futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets G GBIcoin, Demurrage Currencies: Potentially Incitory and Redistributable GBIs (Guaranteed Basic Income initiatives), Demurrage Currencies: Potentially Incitory and Redistributable Gems, Blockchain Development Platforms and APIs, Dapps Genecoin, Blockchain Genomics Genomecoin, Genomecoin, GenomicResearchcoin Genomic Data Commons, Genomecoin, GenomicResearchcoin genomic sequencing, Blockchain Genomics 2.0: Industrialized All-Human-Scale Sequencing Solution-Genomecoin, GenomicResearchcoin GenomicResearchcoin, Genomecoin, GenomicResearchcoin genomics, consumer, Blockchain Genomics-Genomecoin, GenomicResearchcoin Git, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation GitHub, Blockchain Academic Publishing: Journalcoin, Currency Multiplicity: Monetary and Nonmonetary Currencies global public health, Global Public Health: Bitcoin for Contagious Disease Relief GoCoin, Financial Services GoToLunchcoin, Terminology and Concepts governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance decentralized services, Decentralized Governance Services-Decentralized Governance Services dispute resolution, PrecedentCoin: Blockchain Dispute Resolution futarchy, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections personalized governance services, Blockchain Government random-sample elections, Random-Sample Elections societal maturity impact of blockchain governance, Societal Maturity Impact of Blockchain Governance government regulation, Regulatory Status, Government Regulation-Government Regulation Gridcoin, Blockchain Science: Gridcoin, Foldingcoin-Blockchain Science: Gridcoin, Foldingcoin H hashing, Hashing Plus Timestamping-Limitations, Batched Notary Chains as a Class of Blockchain Infrastructure, Technical Challenges Hayek, Friedrich, Communitycoin: Hayek’s Private Currencies Vie for Attention, Demurrage Currencies: Potentially Incitory and Redistributable, Conclusion, The Blockchain Is an Information Technology health, Blockchain Health-Virus Bank, Seed Vault Backup as demurrage currency, Extensibility of Demurrage Concept and Features doctor vendor RFP services, Doctor Vendor RFP Services and Assurance Contracts health notary services, Blockchain Health Notary health research commons , Blockchain Health Research Commons health spending, Healthcoin healthcare decision making and advocacy, Liquid Democracy and Random-Sample Elections personal health record storage, EMRs on the Blockchain: Personal Health Record Storage virus bank and seed vault backup, Virus Bank, Seed Vault Backup Healthcoin, Healthcoin, Demurrage Currencies: Potentially Incitory and Redistributable I identity authentication, eWallet Services and Personal Cryptosecurity, Blockchain 2.0: Contracts, Smart Property, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records Indiegogo, Crowdfunding, Dapps industry scandals, Scandals and Public Perception infrastructure needs and issues, Technical Challenges inheritance gifts, Smart Contracts intellectual property, Monegraph: Online Graphics Protection (see also digital art) Internet administration, Distributed Censorship-Resistant Organizational Models Internet Archive, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Personal Thinking Blockchains Internet censorship prevention (see Decentralized DNS system) Intuit Quickbooks, Merchant Acceptance of Bitcoin IP protection, Hashing Plus Timestamping IPFS project, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation J Johnston, David, Blockchain Technology Could Be Used in the Administration of All Quanta Journalcoin, Blockchain Academic Publishing: Journalcoin Judobaby, Crowdfunding justice applications for censorship-resistant organizational models, Distributed Censorship-Resistant Organizational Models-Distributed Censorship-Resistant Organizational Models digital art, Digital Art: Blockchain Attestation Services (Notary, Intellectual Property Protection)-Personal Thinking Blockchains (see also digital art, blockchain attestation services) digital identity verification, Blockchain 2.0: Contracts, Smart Property, Wallet Development Projects, Digital Identity Verification-Digital Divide of Bitcoin, Limitations, Decentralized Governance Services, Liquid Democracy and Random-Sample Elections, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy, Privacy Challenges for Personal Records freedom of speech/anti-censorship, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel governance, Blockchain Government-Societal Maturity Impact of Blockchain Governance (see also governance) Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection (see also decentralized DNS) K Kickstarter, Crowdfunding, Community Supercomputing Kipochi, Blockchain Neutrality, Global Public Health: Bitcoin for Contagious Disease Relief, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Koinify, Crowdfunding, Dapps Kraken, Financial Services L latency, Blockchain 2.0 Protocol Projects, Technical Challenges, Technical Challenges, Scandals and Public Perception LaZooz, Dapps, Campuscoin, Extensibility of Demurrage Concept and Features Learncoin, Learncoin learning and literacy, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges learning contract exchanges, Learning Contract Exchanges Ledra Capital, Blockchain 2.0: Contracts, Ledra Capital Mega Master Blockchain List legal implications crowdfunding, Crowdfunding smart contracts, Smart Contracts lending, trustless, Smart Property Lighthouse, Crowdfunding limitations, Limitations-Overall: Decentralization Trends Likely to Persist business model challenges, Business Model Challenges government regulation, Government Regulation-Government Regulation personal records privacy challenges, Privacy Challenges for Personal Records scandals and public perception, Scandals and Public Perception-Scandals and Public Perception technical challenges, Technical Challenges-Technical Challenges Liquid Democracy system, Liquid Democracy and Random-Sample Elections-Liquid Democracy and Random-Sample Elections Litecoin, Technology Stack: Blockchain, Protocol, Currency, Technology Stack: Blockchain, Protocol, Currency, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel, Currency Multiplicity: Monetary and Nonmonetary Currencies, Technical Challenges literacy (see learning and literacy) LTBcoin, Wallet Development Projects, Currency, Token, Tokenizing M M2M/IoT infrastructure, M2M/IoT Bitcoin Payment Network to Enable the Machine Economy, Blockchain Development Platforms and APIs, Blockchain Academic Publishing: Journalcoin-The Blockchain Is Not for Every Situation, The Blockchain Is an Information Technology Maidsafe, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Technical Challenges Manna, Crowdfunding marriage, blockchain recorded, Decentralized Governance Services Mastercoin, Blockchain 2.0 Protocol Projects mechanics of cryptocurrencies, How a Cryptocurrency Works Medici, Financial Services mega master blockchain list, Ledra Capital Mega Master Blockchain List-Ledra Capital Mega Master Blockchain List Melotic, Crowdfunding, Wallet Development Projects merchant acceptance, Merchant Acceptance of Bitcoin merchant payment fees, Summary: Blockchain 1.0 in Practical Use messaging, Ethereum: Turing-Complete Virtual Machine, Dapps, Challenges and Other Decentralized DNS Services, Technical Challenges MetaDisk, DAOs and DACs mindfiles, Personal Thinking Blockchains MIT Bitcoin Project, Campuscoin Monegraph, Monegraph: Online Graphics Protection money (see currency) MOOCs (massive open online courses), Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy Moroz, Tatiana, Communitycoin: Hayek’s Private Currencies Vie for Attention multicurrency systems, Demurrage Currencies: Potentially Incitory and Redistributable N Nakamoto, Satoshi, Blockchain 2.0: Contracts, Blockchain 2.0: Contracts Namecoin, Namecoin: Decentralized Domain Name System-Decentralized DNS Functionality Beyond Free Speech: Digital Identity, Monegraph: Online Graphics Protection Nationcoin, Coin Drops as a Strategy for Public Adoption, Demurrage Currencies: Potentially Incitory and Redistributable notary chains, Batched Notary Chains as a Class of Blockchain Infrastructure notary services, Hashing Plus Timestamping, Blockchain Health Notary NSA surveillance, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel NXT, Technology Stack: Blockchain, Protocol, Currency, Blockchain 2.0 Protocol Projects O offline wallets, Technical Challenges OneName, Digital Identity Verification-Digital Identity Verification OneWallet, Wallet Development Projects online graphics protection, Monegraph: Online Graphics Protection-Monegraph: Online Graphics Protection Open Assets, Blockchain 2.0 Protocol Projects Open Transactions, Blockchain 2.0 Protocol Projects OpenBazaar, Dapps, Government Regulation Ostel, Freedom of Speech/Anti-Censorship Applications: Alexandria and Ostel P passports, Decentralized Governance Services PayPal, The Double-Spend and Byzantine Generals’ Computing Problems, Financial Services, Distributed Censorship-Resistant Organizational Models peer-to-peer lending, Financial Services Peercoin, Technology Stack: Blockchain, Protocol, Currency personal cryptosecurity, eWallet Services and Personal Cryptosecurity personal data rights, Blockchain Genomics personal mindfile blockchains, Personal Thinking Blockchains personal thinking chains, Personal Thinking Blockchains-Personal Thinking Blockchains physical asset keys, Blockchain 2.0: Contracts, Smart Property plagiarism detection/avoidance, Blockchain Academic Publishing: Journalcoin Precedent, PrecedentCoin: Blockchain Dispute Resolution, Terminology and Concepts prediction markets, Bitcoin Prediction Markets, DASs and Self-Bootstrapped Organizations, Decentralized Governance Services, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets Predictious, Bitcoin Prediction Markets predictive task automation, Blockchain Layer Could Facilitate Big Data’s Predictive Task Automation privacy challenges, Privacy Challenges for Personal Records private key, eWallet Services and Personal Cryptosecurity Proof of Existence, Proof of Existence-Proof of Existence proof of stake, Blockchain 2.0 Protocol Projects, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges proof of work, PrecedentCoin: Blockchain Dispute Resolution, Technical Challenges-Technical Challenges property ownership, Smart Property property registration, Decentralized Governance Services public documents registries, Decentralized Governance Services public health, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Global Public Health: Bitcoin for Contagious Disease Relief public perception, Scandals and Public Perception-Scandals and Public Perception public/private key cryptography, Public/Private-Key Cryptography 101-Public/Private-Key Cryptography 101 publishing, academic, Blockchain Academic Publishing: Journalcoin-Blockchain Academic Publishing: Journalcoin pull technology, eWallet Services and Personal Cryptosecurity push technology, eWallet Services and Personal Cryptosecurity R random-sample elections, Random-Sample Elections Realcoin, Relation to Fiat Currency redistribution of currency (see demurrage currency) regulation, Government Regulation-Government Regulation regulatory status, Regulatory Status reputation vouching, Ethereum: Turing-Complete Virtual Machine Researchcoin, Blockchain Academic Publishing: Journalcoin REST APIs, Technical Challenges Ripple, Technology Stack: Blockchain, Protocol, Currency, Relation to Fiat Currency, Blockchain 2.0 Protocol Projects Ripple Labs, Financial Services Roadcoin, Blockchain Government S Saldo.mx, Blockchain Neutrality scandals, Scandals and Public Perception science, Blockchain Science: Gridcoin, Foldingcoin-Charity Donations and the Blockchain—Sean’s Outpost community supercomputing, Community Supercomputing global public health, Global Public Health: Bitcoin for Contagious Disease Relief Sean's Outpost, Charity Donations and the Blockchain—Sean’s Outpost secret messaging, Ethereum: Turing-Complete Virtual Machine security issues, Technical Challenges self-bootstrapped organizations, DASs and Self-Bootstrapped Organizations self-directing assets, Automatic Markets and Tradenets self-enforced code, Smart Property self-sufficiency, Smart Contracts SETI@home, Blockchain Science: Gridcoin, Foldingcoin, Community Supercomputing size and bandwidth, Technical Challenges smart contracts, Smart Contracts-Smart Contracts, Smart Contract Advocates on Behalf of Digital Intelligence automatic markets and tradenets, Automatic Markets and Tradenets Counterparty, Counterparty Re-creates Ethereum’s Smart Contract Platform DAOs/DACs, DAOs and DACs-DAOs and DACs Dapps, Dapps-Dapps DASs, DASs and Self-Bootstrapped Organizations Ethereum, Ethereum: Turing-Complete Virtual Machine increasingly autonomous, Dapps, DAOs, DACs, and DASs: Increasingly Autonomous Smart Contracts-Automatic Markets and Tradenets smart literacy contracts, Blockchain Learning: Bitcoin MOOCs and Smart Contract Literacy-Learning Contract Exchanges smart property, Smart Property-Smart Property, Monegraph: Online Graphics Protection smartwatch, Extensibility of Demurrage Concept and Features Snowden, Edward, Distributed Censorship-Resistant Organizational Models social contracts, Smart Contracts social network currencies, Currency Multiplicity: Monetary and Nonmonetary Currencies Stellar, Blockchain Development Platforms and APIs stock market, Financial Services Storj, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation, Dapps, Technical Challenges Stripe, Blockchain Development Platforms and APIs supercomputing, Community Supercomputing Svalbard Global Seed Vault, Virus Bank, Seed Vault Backup Swancoin, Smart Property swaps exchange, Financial Services Swarm, Crowdfunding, Dapps Swarm (Ethereum), Ethereum: Turing-Complete Virtual Machine Swarmops, Crowdfunding T Tatianacoin, Communitycoin: Hayek’s Private Currencies Vie for Attention technical challenges, Technical Challenges-Technical Challenges Tendermint, Technical Challenges Tera Exchange, Financial Services terminology, Terminology and Concepts-Terminology and Concepts 37Coins, Global Public Health: Bitcoin for Contagious Disease Relief throughput, Technical Challenges timestamping, Hashing Plus Timestamping-Limitations titling, Decentralized Governance Services tradenets, Automatic Markets and Tradenets transaction fees, Summary: Blockchain 1.0 in Practical Use Tribecoin, Coin Drops as a Strategy for Public Adoption trustless lending, Smart Property Truthcoin, Futarchy: Two-Step Democracy with Voting + Prediction Markets Turing completeness, Ethereum: Turing-Complete Virtual Machine Twister, Dapps Twitter, Monegraph: Online Graphics Protection U Uber, Government Regulation unbanked/underbanked markets, Blockchain Neutrality usability issues, Technical Challenges V value chain composition, How a Cryptocurrency Works versioning issues, Technical Challenges Virtual Notary, Virtual Notary, Bitnotar, and Chronobit voting and prediction, Futarchy: Two-Step Democracy with Voting + Prediction Markets-Futarchy: Two-Step Democracy with Voting + Prediction Markets W wallet APIs, Blockchain Development Platforms and APIs wallet companies, Wallet Development Projects wallet software, How a Cryptocurrency Works wasted resources, Technical Challenges Wayback Machine, Blockchain Ecosystem: Decentralized Storage, Communication, and Computation Wedbush Securities, Financial Services Whatevercoin, Terminology and Concepts WikiLeaks, Distributed Censorship-Resistant Organizational Models Wikinomics, Community Supercomputing World Citizen project, Decentralized Governance Services X Xapo, eWallet Services and Personal Cryptosecurity Z Zennet Supercomputer, Community Supercomputing Zooko's Triangle, Decentralized DNS Functionality Beyond Free Speech: Digital Identity About the Author Melanie Swan is the Founder of the Institute for Blockchain Studies and a Contemporary Philosophy MA candidate at Kingston University London and Université Paris VIII.

As articulated by cryptographer David Chaum,120 the idea is that (like the ideal of a poll) randomly sampled voters would be more representative (or could at least include underrepresented voters) and give voters more time to deliberate on issues privately at home, seeking their own decision-making resources rather than being swayed by advertising.121 Blockchain technology could be a means of implementing random-sample elections in a large-scale, trustable, pseudonymous way. Futarchy: Two-Step Democracy with Voting + Prediction Markets Another concept is futarchy, a two-level process by which individuals first vote on generally specified outcomes (like “increase GDP”), and second, vote on specific proposals for achieving these outcomes. The first step would be carried out by regular voting processes, the second step via prediction markets. Prediction market voting could be by different cryptocurrencies (the EconomicVotingCoin or EnvironmentalPolicyVotingCoin) or other economically significant tokens. Prediction market voting is investing/speculating, taking a bet on one or the other side of a proposal, betting on the proposal that you want to win.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

"World Economic Forum" Davos, active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, AOL-Time Warner, Bear Stearns, behavioural economics, Black Swan, business cycle, butterfly effect, carbon credits, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Future Shock, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, Herman Kahn, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social contagion, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, tacit knowledge, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, Tragedy of the Commons, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Possibly the disaster could have been averted. These are the sorts of claims that the proponents of prediction markets tend to make, and it’s easy to see why they’ve generated so much interest. In recent years, in fact, prediction markets have been set up to make predictions as varied as the likely success of new products, the box office revenues of upcoming movies, and the outcomes of sporting events. In practice, however, prediction markets are more complicated than the theory suggests. In the 2008 presidential election, for example, one of the most popular prediction markets, Intrade, experienced a series of strange fluctuations when an unknown trader started placing very large bets on John McCain, generating large spikes in the market’s prediction for a McCain victory.

MARKETS, CROWDS, AND MODELS One increasingly popular method is to use what is called a prediction market—meaning a market in which buyers and sellers can trade specially designed securities whose prices correspond to the predicted probability that a specific outcome will take place. For example, the day before the 2008 US presidential election, an investor could have paid $0.92 for a contract in the Iowa Electronic Markets—one of the longest-running and best-known prediction markets—that would have yielded him or her $1 if Barack Obama had won. Participants in prediction markets therefore behave much like participants in financial markets, buying and selling contracts for whatever price is on offer.

Since then, we have either found or learned about the same kind of result for other kinds of events that prediction markets have been used to predict, from the opening weekend box office revenues for feature films to the outcomes of presidential elections. Unlike sports, these events occur without any of the rules or conditions that are designed to make sports competitive. There is also a lot of relevant information that prediction markets could conceivably exploit to boost their performance well beyond that of a simple model or a poll of relatively uninformed individuals. Yet when we compared the Hollywood Stock Exchange (HSX)—one of the most popular prediction markets, which has a reputation for accurate prediction—with a simple statistical model, the HSX did only slightly better.7 And in a separate study of the outcomes of five US presidential elections from 1988 to 2004, political scientists Robert Erikson and Christopher Wlezien found that a simple statistical correction of ordinary opinion polls outperformed even the vaunted Iowa Electronic Markets.8 TRUST NO ONE, ESPECIALLY YOURSELF So what’s going on here?


pages: 1,082 words: 87,792

Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

algorithmic trading, Amazon Web Services, automated trading system, backtesting, barriers to entry, bitcoin, Brownian motion, cloud computing, coronavirus, cryptocurrency, data science, deep learning, Edward Thorp, fiat currency, global macro, Gordon Gekko, Guido van Rossum, implied volatility, information retrieval, margin call, market microstructure, Myron Scholes, natural language processing, paper trading, passive investing, popular electronics, prediction markets, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Rubik’s Cube, seminal paper, Sharpe ratio, short selling, sorting algorithm, systematic trading, transaction costs, value at risk

Index A absolute maximum drawdown, Case Study AdaBoost algorithm, Vectorized Backtesting addition (+) operator, Data Types adjusted return appraisal ratio, Algorithmic Trading algorithmic trading (generally)advantages of, Algorithmic Trading basics, Algorithmic Trading-Algorithmic Trading strategies, Trading Strategies-Conclusions alpha seeking strategies, Trading Strategies alpha, defined, Algorithmic Trading anonymous functions, Python Idioms API key, for data sets, Working with Open Data Sources-Working with Open Data Sources Apple, Inc.intraday stock prices, Getting into the Basics reading stock price data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON retrieving historical unstructured data about, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data app_key, for Eikon Data API, Eikon Data API AQR Capital Management, pandas and the DataFrame Class arithmetic operations, Data Types array programming, Making Use of Vectorization(see also vectorization) automated trading operations, Automating Trading Operations-Strategy Monitoringcapital management, Capital Management-Kelly Criterion for Stocks and Indices configuring Oanda account, Configuring Oanda Account hardware setup, Setting Up the Hardware infrastructure and deployment, Infrastructure and Deployment logging and monitoring, Logging and Monitoring-Logging and Monitoring ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object online algorithm, Online Algorithm-Online Algorithm Python environment setup, Setting Up the Python Environment Python scripts for, Python Script-Strategy Monitoring real-time monitoring, Real-Time Monitoring running code, Running the Code uploading code, Uploading the Code visual step-by-step overview, Visual Step-by-Step Overview-Real-Time Monitoring B backtestingbased on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach Python scripts for classification algorithm backtesting, Classification Algorithm Backtesting Class Python scripts for linear regression backtesting class, Linear Regression Backtesting Class vectorized (see vectorized backtesting) BacktestLongShort class, Long-Short Backtesting Class, Long-Short Backtesting Class bar charts, matplotlib bar plots (see Plotly; streaming bar plot) base class, for event-based backtesting, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class Bash script, Building a Ubuntu and Python Docker Imagefor Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up for Python/Jupyter Lab installation, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Bitcoin, pandas and the DataFrame Class, Working with Open Data Sources Boolean operationsNumPy, Boolean Operations pandas, Boolean Operations C callback functions, Retrieving Streaming Data capital managementautomated trading operations and, Capital Management-Kelly Criterion for Stocks and Indices Kelly criterion for stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Kelly criterion in binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting Carter, Graydon, FX Trading with FXCM CFD (contracts for difference)algorithmic trading risks, Logging and Monitoring defined, CFD Trading with Oanda risks of losses, Long-Short Backtesting Class risks of trading on margin, FX Trading with FXCM trading with Oanda, CFD Trading with Oanda-Python Script(see also Oanda) classification problemsmachine learning for, A Simple Classification Problem-A Simple Classification Problem neural networks for, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited Python scripts for vectorized backtesting, Classification Algorithm Backtesting Class .close_all() method, Placing Orders cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Upinstallation script for Python and Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Jupyter Notebook configuration file, Jupyter Notebook Configuration File RSA public/private keys, RSA Public and Private Keys script to orchestrate Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up Cocteau, Jean, Building Classes for Event-Based Backtesting comma separated value (CSV) files (see CSV files) condaas package manager, Conda as a Package Manager-Basic Operations with Conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager basic operations, Basic Operations with Conda-Basic Operations with Conda installing Miniconda, Installing Miniconda-Installing Miniconda conda remove, Basic Operations with Conda configparser module, The Oanda API containers (see Docker containers) contracts for difference (see CFD) control structures, Control Structures CPython, Python for Finance, Python Infrastructure .create_market_buy_order() method, Placing Orders .create_order() method, Placing Market Orders-Placing Market Orders cross-sectional momentum strategies, Strategies Based on Momentum CSV filesinput-output operations, Input-Output Operations-Input-Output Operations reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python .cummax() method, Case Study currency pairs, Logging and Monitoring(see also EUR/USD exchange rate) algorithmic trading risks, Logging and Monitoring D data science stack, Python, NumPy, matplotlib, pandas data snooping, Data Snooping and Overfitting data storageSQLite3 for, Storing Data with SQLite3-Storing Data with SQLite3 storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects TsTables package for, Using TsTables-Using TsTables data structures, Data Structures-Data Structures DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class, Reading from a CSV File with pandas, DataFrame Class-DataFrame Class DataFrame objectscreating, Vectorization with pandas storing, Storing DataFrame Objects-Storing DataFrame Objects dataism, Preface DatetimeIndex() constructor, Plotting with pandas decision tree classification algorithm, Vectorized Backtesting deep learningadding features to analysis, Adding Different Types of Features-Adding Different Types of Features classification problem, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited deep neural networks for predicting market direction, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features market movement prediction, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features trading strategies and, Machine and Deep Learning deep neural networks, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features delta hedging, Algorithmic Trading dense neural network (DNN), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction dictionary (dict) objects, Reading from a CSV File with Python, Data Structures DigitalOceancloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up droplet setup, Setting Up the Hardware DNN (dense neural network), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Imagebuilding a Ubuntu and Python Docker image, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image defined, Docker Images and Containers Docker images versus, Docker Images and Containers Docker imagesdefined, Docker Images and Containers Docker containers versus, Docker Images and Containers Dockerfile, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image Domingos, Pedro, Automating Trading Operations Droplet, Using Cloud Instancescosts, Infrastructure and Deployment script to orchestrate set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up dynamic hedging, Algorithmic Trading E efficient market hypothesis, Predicting Market Movements with Machine Learning Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Dataretrieving historical structured data, Retrieving Historical Structured Data-Retrieving Historical Structured Data retrieving historical unstructured data, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data Euler discretization, Python Versus Pseudo-Code EUR/USD exchange ratebacktesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars evaluation of regression-based strategy, Generalizing the Approach factoring in leverage/margin, Factoring In Leverage and Margin-Factoring In Leverage and Margin gross performance versus deep learning-based strategy, Using Deep Neural Networks to Predict Market Direction-Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features-Adding Different Types of Features historical ask close prices, Retrieving Historical Data-Retrieving Historical Data historical candles data for, Retrieving Candles Data historical tick data for, Retrieving Tick Data implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time logistic regression-based strategies, Generalizing the Approach placing orders, Placing Orders-Placing Orders predicting, Predicting Index Levels-Predicting Index Levels predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels retrieving streaming data for, Retrieving Streaming Data retrieving trading account information, Retrieving Account Information-Retrieving Account Information SMA calculation, Getting into the Basics-Generalizing the Approach vectorized backtesting of ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy event-based backtesting, Building Classes for Event-Based Backtesting-Long-Short Backtesting Classadvantages, Building Classes for Event-Based Backtesting base class, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class building classes for, Building Classes for Event-Based Backtesting-Long-Short Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class Python scripts for, Backtesting Base Class-Long-Short Backtesting Class Excelexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON F featuresadding different types, Adding Different Types of Features-Adding Different Types of Features lags and, Using Logistic Regression to Predict Market Direction financial data, working with, Working with Financial Data-Python Scriptsdata set for examples, The Data Set Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Data exporting to Excel/JSON, Exporting to Excel and JSON open data sources, Working with Open Data Sources-Working with Open Data Sources reading data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON reading data from Excel/JSON, Reading from Excel and JSON reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 .flatten() method, matplotlib foreign exchange trading (see FX trading; FXCM) future returns, predicting, Predicting Future Returns-Predicting Future Returns FX trading, FX Trading with FXCM-References and Further Resources(see also EUR/USD exchange rate) FXCMFX trading, FX Trading with FXCM-References and Further Resources getting started, Getting Started placing orders, Placing Orders-Placing Orders retrieving account information, Account Information retrieving candles data, Retrieving Candles Data-Retrieving Candles Data retrieving data, Retrieving Data-Retrieving Candles Data retrieving historical data, Retrieving Historical Data-Retrieving Historical Data retrieving streaming data, Retrieving Streaming Data retrieving tick data, Retrieving Tick Data-Retrieving Tick Data working with the API, Working with the API-Account Information fxcmpy wrapper packagecallback functions, Retrieving Streaming Data installing, Getting Started tick data retrieval, Retrieving Tick Data fxTrade, CFD Trading with Oanda G GDX (VanEck Vectors Gold Miners ETF)logistic regression-based strategies, Generalizing the Approach mean-reversion strategies, Getting into the Basics-Generalizing the Approach regression-based strategies, Generalizing the Approach generate_sample_data(), Storing Financial Data Efficiently .get_account_summary() method, Retrieving Account Information .get_candles() method, Retrieving Historical Data .get_data() method, Backtesting Base Class, Retrieving Tick Data .get_date_price() method, Backtesting Base Class .get_instruments() method, Looking Up Instruments Available for Trading .get_last_price() method, Retrieving Streaming Data .get_raw_data() method, Retrieving Tick Data get_timeseries() function, Retrieving Historical Structured Data .get_transactions() method, Retrieving Account Information GLD (SPDR Gold Shares)logistic regression-based strategies, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction mean-reversion strategies, Getting into the Basics-Generalizing the Approach gold pricemean-reversion strategies, Getting into the Basics-Getting into the Basics momentum strategy and, Getting into the Basics-Getting into the Basics, Generalizing the Approach-Generalizing the Approach Goldman Sachs, Python and Algorithmic Trading, Algorithmic Trading .go_long() method, Long-Short Backtesting Class H half Kelly criterion, Optimal Leverage Harari, Yuval Noah, Preface HDF5 binary storage library, Using TsTables-Using TsTables HDFStore wrapper, Storing DataFrame Objects-Storing DataFrame Objects high frequency trading (HFQ), Algorithmic Trading histograms, matplotlib hit ratio, defined, Vectorized Backtesting I if-elif-else control structure, Python Idioms in-sample fitting, Generalizing the Approach index levels, predicting, Predicting Index Levels-Predicting Index Levels infrastructure (see Python infrastructure) installation script, Python/Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Intel Math Kernel Library, Basic Operations with Conda iterations, Control Structures J JSONexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON Jupyter Labinstallation script for, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab RSA public/private keys for, RSA Public and Private Keys tools included, Using Cloud Instances Jupyter Notebook, Jupyter Notebook Configuration File K Kelly criterionin binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting optimal leverage, Optimal Leverage-Optimal Leverage stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Keras, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features key-value stores, Data Structures keys, public/private, RSA Public and Private Keys L lags, The Basic Idea for Price Prediction, Using Logistic Regression to Predict Market Direction lambda functions, Python Idioms LaTeX, Python Versus Pseudo-Code leveraged trading, risks of, Factoring In Leverage and Margin, FX Trading with FXCM, Optimal Leverage linear regressiongeneralizing the approach, Generalizing the Approach market movement prediction, Using Linear Regression for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction review of, A Quick Review of Linear Regression scikit-learn and, Linear Regression with scikit-learn vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy, Linear Regression Backtesting Class list comprehension, Python Idioms list constructor, Data Structures list objects, Reading from a CSV File with Python, Data Structures, Regular ndarray Object logging, of automated trading operations, Logging and Monitoring-Logging and Monitoring logistic regressiongeneralizing the approach, Generalizing the Approach-Generalizing the Approach market direction prediction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction Python script for vectorized backtesting, Classification Algorithm Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class longest drawdown period, Risk Analysis M machine learningclassification problem, A Simple Classification Problem-A Simple Classification Problem linear regression with scikit-learn, Linear Regression with scikit-learn market movement prediction, Using Machine Learning for Market Movement Prediction-Generalizing the Approach ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object Python scripts, Linear Regression Backtesting Class trading strategies and, Machine and Deep Learning using logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction macro hedge funds, algorithmic trading and, Algorithmic Trading __main__ method, Backtesting Base Class margin trading, FX Trading with FXCM market direction prediction, Predicting Future Market Direction market movement predictiondeep learning for, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features deep neural networks for, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features linear regression for, Using Linear Regression for Market Movement Prediction-Generalizing the Approach linear regression with scikit-learn, Linear Regression with scikit-learn logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction machine learning for, Using Machine Learning for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy market orders, placing, Placing Market Orders-Placing Market Orders math module, Data Types mathematical functions, Data Types matplotlib, matplotlib-matplotlib, Plotting with pandas-Plotting with pandas maximum drawdown, Risk Analysis, Case Study McKinney, Wes, pandas and the DataFrame Class mean-reversion strategies, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approachbasics, Getting into the Basics-Generalizing the Approach generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Miniconda, Installing Miniconda-Installing Miniconda mkl (Intel Math Kernel Library), Basic Operations with Conda ML-based strategies, ML-Based Trading Strategy-Persisting the Model Objectoptimal leverage, Optimal Leverage-Optimal Leverage persisting the model object, Persisting the Model Object Python script for, Automated Trading Strategy risk analysis, Risk Analysis-Risk Analysis vectorized backtesting, Vectorized Backtesting-Vectorized Backtesting MLPClassifier, The Simple Classification Problem Revisited MLTrader class, Online Algorithm-Online Algorithm momentum strategies, Momentumbacktesting on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars basics, Getting into the Basics-Getting into the Basics generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Python script for custom streaming class, Python Script Python script for momentum online algorithm, Momentum Online Algorithm vectorized backtesting of, Strategies Based on Momentum-Generalizing the Approach MomentumTrader class, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time MomVectorBacktester class, Generalizing the Approach monitoringautomated trading operations, Logging and Monitoring-Logging and Monitoring, Real-Time Monitoring Python scripts for strategy monitoring, Strategy Monitoring Monte Carlo simulationsample tick data server, Sample Tick Data Server time series data based on, Python Scripts motives, for trading, Algorithmic Trading MRVectorBacktester class, Generalizing the Approach multi-layer perceptron, The Simple Classification Problem Revisited Musashi, Miyamoto, Python Infrastructure N natural language processing (NLP), Retrieving Historical Unstructured Data ndarray class, Vectorization with NumPy-Vectorization with NumPy ndarray objects, NumPy and Vectorization, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functionscreating, ndarray Creation linear regression and, A Quick Review of Linear Regression regular, Regular ndarray Object nested structures, Data Structures NLP (natural language processing), Retrieving Historical Unstructured Data np.arange(), ndarray Creation numbers, data typing of, Data Types numerical operations, pandas, Numerical Operations NumPy, NumPy and Vectorization-NumPy and Vectorization, NumPy-Random NumbersBoolean operations, Boolean Operations ndarray creation, ndarray Creation ndarray methods, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functions random numbers, Random Numbers regular ndarray object, Regular ndarray Object universal functions, ndarray Methods and NumPy Functions vectorization, Vectorization with NumPy-Vectorization with NumPy vectorized operations, Vectorized Operations numpy.random sub-package, Random Numbers NYSE Arca Gold Miners Index, Getting into the Basics O Oandaaccount configuration, Configuring Oanda Account account setup, Setting Up an Account API access, The Oanda API-The Oanda API backtesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars CFD trading, CFD Trading with Oanda-Python Script factoring in leverage/margin with historical data, Factoring In Leverage and Margin-Factoring In Leverage and Margin implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time looking up instruments available for trading, Looking Up Instruments Available for Trading placing market orders, Placing Market Orders-Placing Market Orders Python script for custom streaming class, Python Script retrieving account information, Retrieving Account Information-Retrieving Account Information retrieving historical data, Retrieving Historical Data-Factoring In Leverage and Margin working with streaming data, Working with Streaming Data Oanda v20 RESTful API, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting offline algorithmdefined, Signal Generation in Real Time transformation to online algorithm, Online Algorithm OLS (ordinary least squares) regression, matplotlib online algorithmautomated trading operations, Online Algorithm-Online Algorithm defined, Signal Generation in Real Time Python script for momentum online algorithm, Momentum Online Algorithm signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time transformation of offline algorithm to, Online Algorithm .on_success() method, Implementing Trading Strategies in Real Time, Online Algorithm open data sources, Working with Open Data Sources-Working with Open Data Sources ordinary least squares (OLS) regression, matplotlib out-of-sample evaluation, Generalizing the Approach overfitting, Data Snooping and Overfitting P package manager, conda as, Conda as a Package Manager-Basic Operations with Conda pandas, pandas and the DataFrame Class-pandas and the DataFrame Class, pandas-Input-Output OperationsBoolean operations, Boolean Operations case study, Case Study-Case Study data selection, Data Selection-Data Selection DataFrame class, DataFrame Class-DataFrame Class exporting financial data to Excel/JSON, Exporting to Excel and JSON input-output operations, Input-Output Operations-Input-Output Operations numerical operations, Numerical Operations plotting, Plotting with pandas-Plotting with pandas reading financial data from Excel/JSON, Reading from Excel and JSON reading from a CSV file, Reading from a CSV File with pandas storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects vectorization, Vectorization with pandas-Vectorization with pandas password protection, for Jupyter lab, Jupyter Notebook Configuration File .place_buy_order() method, Backtesting Base Class .place_sell_order() method, Backtesting Base Class Plotlybasics, The Basics multiple real-time streams for, Three Real-Time Streams multiple sub-plots for streams, Three Sub-Plots for Three Streams streaming data as bars, Streaming Data as Bars visualization of streaming data, Visualizing Streaming Data with Plotly-Streaming Data as Bars plotting, with pandas, Plotting with pandas-Plotting with pandas .plot_data() method, Backtesting Base Class polyfit()/polyval() convenience functions, matplotlib price prediction, based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction .print_balance() method, Backtesting Base Class .print_net_wealth() method, Backtesting Base Class .print_transactions() method, Retrieving Account Information pseudo-code, Python versus, Python Versus Pseudo-Code publisher-subscriber (PUB-SUB) pattern, Working with Real-Time Data and Sockets Python (generally)advantages of, Python for Algorithmic Trading basics, Python and Algorithmic Trading-References and Further Resources control structures, Control Structures data structures, Data Structures-Data Structures data types, Data Types-Data Types deployment difficulties, Python Infrastructure idioms, Python Idioms-Python Idioms NumPy and vectorization, NumPy and Vectorization-NumPy and Vectorization obstacles to adoption in financial industry, Python for Finance origins, Python for Finance pandas and DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class pseudo-code versus, Python Versus Pseudo-Code reading from a CSV file, Reading from a CSV File with Python-Reading from a CSV File with Python Python infrastructure, Python Infrastructure-References and Further Resourcesconda as package manager, Conda as a Package Manager-Basic Operations with Conda conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Image using cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up Python scriptsautomated trading operations, Running the Code, Python Script-Strategy Monitoring backtesting base class, Backtesting Base Class custom streaming class that trades a momentum strategy, Python Script linear regression backtesting class, Linear Regression Backtesting Class long-only backtesting class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class real-time data handling, Python Scripts-Sample Data Server for Bar Plot sample time series data set, Python Scripts strategy monitoring, Strategy Monitoring uploading for automated trading operations, Uploading the Code vectorized backtesting, Python Scripts-Mean Reversion Backtesting Class Q Quandlpremium data sets, Working with Open Data Sources working with open data sources, Working with Open Data Sources-Working with Open Data Sources R random numbers, Random Numbers random walk hypothesis, Predicting Index Levels range (iterator object), Control Structures read_csv() function, Reading from a CSV File with pandas real-time data, Working with Real-Time Data and Sockets-Sample Data Server for Bar PlotPython script for handling, Python Scripts-Sample Data Server for Bar Plot signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time tick data client for, Connecting a Simple Tick Data Client tick data server for, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server visualizing streaming data with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars real-time monitoring, Real-Time Monitoring Refinitiv, Eikon Data API relative maximum drawdown, Case Study returns, predicting future, Predicting Future Returns-Predicting Future Returns risk analysis, for ML-based trading strategy, Risk Analysis-Risk Analysis RSA public/private keys, RSA Public and Private Keys .run_mean_reversion_strategy() method, Long-Only Backtesting Class, Long-Short Backtesting Class .run_simulation() method, Kelly Criterion in Binomial Setting S S&P 500, Algorithmic Trading-Algorithmic Tradinglogistic regression-based strategies and, Generalizing the Approach momentum strategies, Getting into the Basics passive long position in, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices scatter objects, Three Real-Time Streams scientific stack, NumPy and Vectorization, Python, NumPy, matplotlib, pandas scikit-learn, Linear Regression with scikit-learn ScikitBacktester class, Generalizing the Approach-Generalizing the Approach SciPy package project, NumPy and Vectorization seaborn library, matplotlib-matplotlib simple moving averages (SMAs), pandas and the DataFrame Class, Simple Moving Averagestrading strategies based on, Strategies Based on Simple Moving Averages-Generalizing the Approach visualization with price ticks, Three Real-Time Streams .simulate_value() method, Running a Simple Tick Data Server Singer, Paul, CFD Trading with Oanda sockets, real-time data and, Working with Real-Time Data and Sockets-Sample Data Server for Bar Plot sorting list objects, Data Structures SQLite3, Storing Data with SQLite3-Storing Data with SQLite3 SSL certificate, RSA Public and Private Keys storage (see data storage) streaming bar plots, Streaming Data as Bars, Sample Data Server for Bar Plot streaming dataOanda and, Working with Streaming Data visualization with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars string objects (str), Data Types-Data Types Swiss Franc event, CFD Trading with Oanda systematic macro hedge funds, Algorithmic Trading T TensorFlow, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction Thomas, Rob, Working with Financial Data Thorp, Edward, Capital Management tick data client, Connecting a Simple Tick Data Client tick data server, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server time series data setspandas and vectorization, Vectorization with pandas price prediction based on, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction Python script for generating sample set, Python Scripts SQLite3 for storage of, Storing Data with SQLite3-Storing Data with SQLite3 TsTables for storing, Using TsTables-Using TsTables time series momentum strategies, Strategies Based on Momentum(see also momentum strategies) .to_hdf() method, Storing DataFrame Objects tpqoa wrapper package, The Oanda API, Working with Streaming Data trading platforms, factors influencing choice of, CFD Trading with Oanda trading strategies, Trading Strategies-Conclusions(see also specific strategies) implementing in real time with Oanda, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time machine learning/deep learning, Machine and Deep Learning mean-reversion, NumPy and Vectorization momentum, Momentum simple moving averages, Simple Moving Averages trading, motives for, Algorithmic Trading transaction costs, Long-Only Backtesting Class, Vectorized Backtesting TsTables package, Using TsTables-Using TsTables tuple objects, Data Structures U Ubuntu, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image universal functions, NumPy, ndarray Methods and NumPy Functions V v20 wrapper package, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting value-at-risk (VAR), Risk Analysis-Risk Analysis vectorization, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approach vectorized backtestingdata snooping and overfitting, Data Snooping and Overfitting-Conclusions ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting momentum-based trading strategies, Strategies Based on Momentum-Generalizing the Approach potential shortcomings, Building Classes for Event-Based Backtesting Python code with a class for vectorized backtesting of mean-reversion trading strategies, Momentum Backtesting Class Python scripts for, Python Scripts-Mean Reversion Backtesting Class, Linear Regression Backtesting Class regression-based strategy, Vectorized Backtesting of Regression-Based Strategy trading strategies based on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach vectorization with NumPy, Vectorization with NumPy-Vectorization with NumPy vectorization with pandas, Vectorization with pandas-Vectorization with pandas vectorized operations, Vectorized Operations virtual environment management, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager W while loops, Control Structures Z ZeroMQ, Working with Real-Time Data and Sockets About the Author Dr.

Index A absolute maximum drawdown, Case Study AdaBoost algorithm, Vectorized Backtesting addition (+) operator, Data Types adjusted return appraisal ratio, Algorithmic Trading algorithmic trading (generally)advantages of, Algorithmic Trading basics, Algorithmic Trading-Algorithmic Trading strategies, Trading Strategies-Conclusions alpha seeking strategies, Trading Strategies alpha, defined, Algorithmic Trading anonymous functions, Python Idioms API key, for data sets, Working with Open Data Sources-Working with Open Data Sources Apple, Inc.intraday stock prices, Getting into the Basics reading stock price data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON retrieving historical unstructured data about, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data app_key, for Eikon Data API, Eikon Data API AQR Capital Management, pandas and the DataFrame Class arithmetic operations, Data Types array programming, Making Use of Vectorization(see also vectorization) automated trading operations, Automating Trading Operations-Strategy Monitoringcapital management, Capital Management-Kelly Criterion for Stocks and Indices configuring Oanda account, Configuring Oanda Account hardware setup, Setting Up the Hardware infrastructure and deployment, Infrastructure and Deployment logging and monitoring, Logging and Monitoring-Logging and Monitoring ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object online algorithm, Online Algorithm-Online Algorithm Python environment setup, Setting Up the Python Environment Python scripts for, Python Script-Strategy Monitoring real-time monitoring, Real-Time Monitoring running code, Running the Code uploading code, Uploading the Code visual step-by-step overview, Visual Step-by-Step Overview-Real-Time Monitoring B backtestingbased on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach Python scripts for classification algorithm backtesting, Classification Algorithm Backtesting Class Python scripts for linear regression backtesting class, Linear Regression Backtesting Class vectorized (see vectorized backtesting) BacktestLongShort class, Long-Short Backtesting Class, Long-Short Backtesting Class bar charts, matplotlib bar plots (see Plotly; streaming bar plot) base class, for event-based backtesting, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class Bash script, Building a Ubuntu and Python Docker Imagefor Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up for Python/Jupyter Lab installation, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Bitcoin, pandas and the DataFrame Class, Working with Open Data Sources Boolean operationsNumPy, Boolean Operations pandas, Boolean Operations C callback functions, Retrieving Streaming Data capital managementautomated trading operations and, Capital Management-Kelly Criterion for Stocks and Indices Kelly criterion for stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Kelly criterion in binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting Carter, Graydon, FX Trading with FXCM CFD (contracts for difference)algorithmic trading risks, Logging and Monitoring defined, CFD Trading with Oanda risks of losses, Long-Short Backtesting Class risks of trading on margin, FX Trading with FXCM trading with Oanda, CFD Trading with Oanda-Python Script(see also Oanda) classification problemsmachine learning for, A Simple Classification Problem-A Simple Classification Problem neural networks for, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited Python scripts for vectorized backtesting, Classification Algorithm Backtesting Class .close_all() method, Placing Orders cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Upinstallation script for Python and Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Jupyter Notebook configuration file, Jupyter Notebook Configuration File RSA public/private keys, RSA Public and Private Keys script to orchestrate Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up Cocteau, Jean, Building Classes for Event-Based Backtesting comma separated value (CSV) files (see CSV files) condaas package manager, Conda as a Package Manager-Basic Operations with Conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager basic operations, Basic Operations with Conda-Basic Operations with Conda installing Miniconda, Installing Miniconda-Installing Miniconda conda remove, Basic Operations with Conda configparser module, The Oanda API containers (see Docker containers) contracts for difference (see CFD) control structures, Control Structures CPython, Python for Finance, Python Infrastructure .create_market_buy_order() method, Placing Orders .create_order() method, Placing Market Orders-Placing Market Orders cross-sectional momentum strategies, Strategies Based on Momentum CSV filesinput-output operations, Input-Output Operations-Input-Output Operations reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python .cummax() method, Case Study currency pairs, Logging and Monitoring(see also EUR/USD exchange rate) algorithmic trading risks, Logging and Monitoring D data science stack, Python, NumPy, matplotlib, pandas data snooping, Data Snooping and Overfitting data storageSQLite3 for, Storing Data with SQLite3-Storing Data with SQLite3 storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects TsTables package for, Using TsTables-Using TsTables data structures, Data Structures-Data Structures DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class, Reading from a CSV File with pandas, DataFrame Class-DataFrame Class DataFrame objectscreating, Vectorization with pandas storing, Storing DataFrame Objects-Storing DataFrame Objects dataism, Preface DatetimeIndex() constructor, Plotting with pandas decision tree classification algorithm, Vectorized Backtesting deep learningadding features to analysis, Adding Different Types of Features-Adding Different Types of Features classification problem, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited deep neural networks for predicting market direction, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features market movement prediction, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features trading strategies and, Machine and Deep Learning deep neural networks, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features delta hedging, Algorithmic Trading dense neural network (DNN), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction dictionary (dict) objects, Reading from a CSV File with Python, Data Structures DigitalOceancloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up droplet setup, Setting Up the Hardware DNN (dense neural network), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Imagebuilding a Ubuntu and Python Docker image, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image defined, Docker Images and Containers Docker images versus, Docker Images and Containers Docker imagesdefined, Docker Images and Containers Docker containers versus, Docker Images and Containers Dockerfile, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image Domingos, Pedro, Automating Trading Operations Droplet, Using Cloud Instancescosts, Infrastructure and Deployment script to orchestrate set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up dynamic hedging, Algorithmic Trading E efficient market hypothesis, Predicting Market Movements with Machine Learning Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Dataretrieving historical structured data, Retrieving Historical Structured Data-Retrieving Historical Structured Data retrieving historical unstructured data, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data Euler discretization, Python Versus Pseudo-Code EUR/USD exchange ratebacktesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars evaluation of regression-based strategy, Generalizing the Approach factoring in leverage/margin, Factoring In Leverage and Margin-Factoring In Leverage and Margin gross performance versus deep learning-based strategy, Using Deep Neural Networks to Predict Market Direction-Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features-Adding Different Types of Features historical ask close prices, Retrieving Historical Data-Retrieving Historical Data historical candles data for, Retrieving Candles Data historical tick data for, Retrieving Tick Data implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time logistic regression-based strategies, Generalizing the Approach placing orders, Placing Orders-Placing Orders predicting, Predicting Index Levels-Predicting Index Levels predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels retrieving streaming data for, Retrieving Streaming Data retrieving trading account information, Retrieving Account Information-Retrieving Account Information SMA calculation, Getting into the Basics-Generalizing the Approach vectorized backtesting of ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy event-based backtesting, Building Classes for Event-Based Backtesting-Long-Short Backtesting Classadvantages, Building Classes for Event-Based Backtesting base class, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class building classes for, Building Classes for Event-Based Backtesting-Long-Short Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class Python scripts for, Backtesting Base Class-Long-Short Backtesting Class Excelexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON F featuresadding different types, Adding Different Types of Features-Adding Different Types of Features lags and, Using Logistic Regression to Predict Market Direction financial data, working with, Working with Financial Data-Python Scriptsdata set for examples, The Data Set Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Data exporting to Excel/JSON, Exporting to Excel and JSON open data sources, Working with Open Data Sources-Working with Open Data Sources reading data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON reading data from Excel/JSON, Reading from Excel and JSON reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 .flatten() method, matplotlib foreign exchange trading (see FX trading; FXCM) future returns, predicting, Predicting Future Returns-Predicting Future Returns FX trading, FX Trading with FXCM-References and Further Resources(see also EUR/USD exchange rate) FXCMFX trading, FX Trading with FXCM-References and Further Resources getting started, Getting Started placing orders, Placing Orders-Placing Orders retrieving account information, Account Information retrieving candles data, Retrieving Candles Data-Retrieving Candles Data retrieving data, Retrieving Data-Retrieving Candles Data retrieving historical data, Retrieving Historical Data-Retrieving Historical Data retrieving streaming data, Retrieving Streaming Data retrieving tick data, Retrieving Tick Data-Retrieving Tick Data working with the API, Working with the API-Account Information fxcmpy wrapper packagecallback functions, Retrieving Streaming Data installing, Getting Started tick data retrieval, Retrieving Tick Data fxTrade, CFD Trading with Oanda G GDX (VanEck Vectors Gold Miners ETF)logistic regression-based strategies, Generalizing the Approach mean-reversion strategies, Getting into the Basics-Generalizing the Approach regression-based strategies, Generalizing the Approach generate_sample_data(), Storing Financial Data Efficiently .get_account_summary() method, Retrieving Account Information .get_candles() method, Retrieving Historical Data .get_data() method, Backtesting Base Class, Retrieving Tick Data .get_date_price() method, Backtesting Base Class .get_instruments() method, Looking Up Instruments Available for Trading .get_last_price() method, Retrieving Streaming Data .get_raw_data() method, Retrieving Tick Data get_timeseries() function, Retrieving Historical Structured Data .get_transactions() method, Retrieving Account Information GLD (SPDR Gold Shares)logistic regression-based strategies, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction mean-reversion strategies, Getting into the Basics-Generalizing the Approach gold pricemean-reversion strategies, Getting into the Basics-Getting into the Basics momentum strategy and, Getting into the Basics-Getting into the Basics, Generalizing the Approach-Generalizing the Approach Goldman Sachs, Python and Algorithmic Trading, Algorithmic Trading .go_long() method, Long-Short Backtesting Class H half Kelly criterion, Optimal Leverage Harari, Yuval Noah, Preface HDF5 binary storage library, Using TsTables-Using TsTables HDFStore wrapper, Storing DataFrame Objects-Storing DataFrame Objects high frequency trading (HFQ), Algorithmic Trading histograms, matplotlib hit ratio, defined, Vectorized Backtesting I if-elif-else control structure, Python Idioms in-sample fitting, Generalizing the Approach index levels, predicting, Predicting Index Levels-Predicting Index Levels infrastructure (see Python infrastructure) installation script, Python/Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Intel Math Kernel Library, Basic Operations with Conda iterations, Control Structures J JSONexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON Jupyter Labinstallation script for, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab RSA public/private keys for, RSA Public and Private Keys tools included, Using Cloud Instances Jupyter Notebook, Jupyter Notebook Configuration File K Kelly criterionin binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting optimal leverage, Optimal Leverage-Optimal Leverage stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Keras, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features key-value stores, Data Structures keys, public/private, RSA Public and Private Keys L lags, The Basic Idea for Price Prediction, Using Logistic Regression to Predict Market Direction lambda functions, Python Idioms LaTeX, Python Versus Pseudo-Code leveraged trading, risks of, Factoring In Leverage and Margin, FX Trading with FXCM, Optimal Leverage linear regressiongeneralizing the approach, Generalizing the Approach market movement prediction, Using Linear Regression for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction review of, A Quick Review of Linear Regression scikit-learn and, Linear Regression with scikit-learn vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy, Linear Regression Backtesting Class list comprehension, Python Idioms list constructor, Data Structures list objects, Reading from a CSV File with Python, Data Structures, Regular ndarray Object logging, of automated trading operations, Logging and Monitoring-Logging and Monitoring logistic regressiongeneralizing the approach, Generalizing the Approach-Generalizing the Approach market direction prediction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction Python script for vectorized backtesting, Classification Algorithm Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class longest drawdown period, Risk Analysis M machine learningclassification problem, A Simple Classification Problem-A Simple Classification Problem linear regression with scikit-learn, Linear Regression with scikit-learn market movement prediction, Using Machine Learning for Market Movement Prediction-Generalizing the Approach ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object Python scripts, Linear Regression Backtesting Class trading strategies and, Machine and Deep Learning using logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction macro hedge funds, algorithmic trading and, Algorithmic Trading __main__ method, Backtesting Base Class margin trading, FX Trading with FXCM market direction prediction, Predicting Future Market Direction market movement predictiondeep learning for, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features deep neural networks for, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features linear regression for, Using Linear Regression for Market Movement Prediction-Generalizing the Approach linear regression with scikit-learn, Linear Regression with scikit-learn logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction machine learning for, Using Machine Learning for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy market orders, placing, Placing Market Orders-Placing Market Orders math module, Data Types mathematical functions, Data Types matplotlib, matplotlib-matplotlib, Plotting with pandas-Plotting with pandas maximum drawdown, Risk Analysis, Case Study McKinney, Wes, pandas and the DataFrame Class mean-reversion strategies, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approachbasics, Getting into the Basics-Generalizing the Approach generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Miniconda, Installing Miniconda-Installing Miniconda mkl (Intel Math Kernel Library), Basic Operations with Conda ML-based strategies, ML-Based Trading Strategy-Persisting the Model Objectoptimal leverage, Optimal Leverage-Optimal Leverage persisting the model object, Persisting the Model Object Python script for, Automated Trading Strategy risk analysis, Risk Analysis-Risk Analysis vectorized backtesting, Vectorized Backtesting-Vectorized Backtesting MLPClassifier, The Simple Classification Problem Revisited MLTrader class, Online Algorithm-Online Algorithm momentum strategies, Momentumbacktesting on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars basics, Getting into the Basics-Getting into the Basics generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Python script for custom streaming class, Python Script Python script for momentum online algorithm, Momentum Online Algorithm vectorized backtesting of, Strategies Based on Momentum-Generalizing the Approach MomentumTrader class, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time MomVectorBacktester class, Generalizing the Approach monitoringautomated trading operations, Logging and Monitoring-Logging and Monitoring, Real-Time Monitoring Python scripts for strategy monitoring, Strategy Monitoring Monte Carlo simulationsample tick data server, Sample Tick Data Server time series data based on, Python Scripts motives, for trading, Algorithmic Trading MRVectorBacktester class, Generalizing the Approach multi-layer perceptron, The Simple Classification Problem Revisited Musashi, Miyamoto, Python Infrastructure N natural language processing (NLP), Retrieving Historical Unstructured Data ndarray class, Vectorization with NumPy-Vectorization with NumPy ndarray objects, NumPy and Vectorization, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functionscreating, ndarray Creation linear regression and, A Quick Review of Linear Regression regular, Regular ndarray Object nested structures, Data Structures NLP (natural language processing), Retrieving Historical Unstructured Data np.arange(), ndarray Creation numbers, data typing of, Data Types numerical operations, pandas, Numerical Operations NumPy, NumPy and Vectorization-NumPy and Vectorization, NumPy-Random NumbersBoolean operations, Boolean Operations ndarray creation, ndarray Creation ndarray methods, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functions random numbers, Random Numbers regular ndarray object, Regular ndarray Object universal functions, ndarray Methods and NumPy Functions vectorization, Vectorization with NumPy-Vectorization with NumPy vectorized operations, Vectorized Operations numpy.random sub-package, Random Numbers NYSE Arca Gold Miners Index, Getting into the Basics O Oandaaccount configuration, Configuring Oanda Account account setup, Setting Up an Account API access, The Oanda API-The Oanda API backtesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars CFD trading, CFD Trading with Oanda-Python Script factoring in leverage/margin with historical data, Factoring In Leverage and Margin-Factoring In Leverage and Margin implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time looking up instruments available for trading, Looking Up Instruments Available for Trading placing market orders, Placing Market Orders-Placing Market Orders Python script for custom streaming class, Python Script retrieving account information, Retrieving Account Information-Retrieving Account Information retrieving historical data, Retrieving Historical Data-Factoring In Leverage and Margin working with streaming data, Working with Streaming Data Oanda v20 RESTful API, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting offline algorithmdefined, Signal Generation in Real Time transformation to online algorithm, Online Algorithm OLS (ordinary least squares) regression, matplotlib online algorithmautomated trading operations, Online Algorithm-Online Algorithm defined, Signal Generation in Real Time Python script for momentum online algorithm, Momentum Online Algorithm signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time transformation of offline algorithm to, Online Algorithm .on_success() method, Implementing Trading Strategies in Real Time, Online Algorithm open data sources, Working with Open Data Sources-Working with Open Data Sources ordinary least squares (OLS) regression, matplotlib out-of-sample evaluation, Generalizing the Approach overfitting, Data Snooping and Overfitting P package manager, conda as, Conda as a Package Manager-Basic Operations with Conda pandas, pandas and the DataFrame Class-pandas and the DataFrame Class, pandas-Input-Output OperationsBoolean operations, Boolean Operations case study, Case Study-Case Study data selection, Data Selection-Data Selection DataFrame class, DataFrame Class-DataFrame Class exporting financial data to Excel/JSON, Exporting to Excel and JSON input-output operations, Input-Output Operations-Input-Output Operations numerical operations, Numerical Operations plotting, Plotting with pandas-Plotting with pandas reading financial data from Excel/JSON, Reading from Excel and JSON reading from a CSV file, Reading from a CSV File with pandas storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects vectorization, Vectorization with pandas-Vectorization with pandas password protection, for Jupyter lab, Jupyter Notebook Configuration File .place_buy_order() method, Backtesting Base Class .place_sell_order() method, Backtesting Base Class Plotlybasics, The Basics multiple real-time streams for, Three Real-Time Streams multiple sub-plots for streams, Three Sub-Plots for Three Streams streaming data as bars, Streaming Data as Bars visualization of streaming data, Visualizing Streaming Data with Plotly-Streaming Data as Bars plotting, with pandas, Plotting with pandas-Plotting with pandas .plot_data() method, Backtesting Base Class polyfit()/polyval() convenience functions, matplotlib price prediction, based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction .print_balance() method, Backtesting Base Class .print_net_wealth() method, Backtesting Base Class .print_transactions() method, Retrieving Account Information pseudo-code, Python versus, Python Versus Pseudo-Code publisher-subscriber (PUB-SUB) pattern, Working with Real-Time Data and Sockets Python (generally)advantages of, Python for Algorithmic Trading basics, Python and Algorithmic Trading-References and Further Resources control structures, Control Structures data structures, Data Structures-Data Structures data types, Data Types-Data Types deployment difficulties, Python Infrastructure idioms, Python Idioms-Python Idioms NumPy and vectorization, NumPy and Vectorization-NumPy and Vectorization obstacles to adoption in financial industry, Python for Finance origins, Python for Finance pandas and DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class pseudo-code versus, Python Versus Pseudo-Code reading from a CSV file, Reading from a CSV File with Python-Reading from a CSV File with Python Python infrastructure, Python Infrastructure-References and Further Resourcesconda as package manager, Conda as a Package Manager-Basic Operations with Conda conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Image using cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up Python scriptsautomated trading operations, Running the Code, Python Script-Strategy Monitoring backtesting base class, Backtesting Base Class custom streaming class that trades a momentum strategy, Python Script linear regression backtesting class, Linear Regression Backtesting Class long-only backtesting class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class real-time data handling, Python Scripts-Sample Data Server for Bar Plot sample time series data set, Python Scripts strategy monitoring, Strategy Monitoring uploading for automated trading operations, Uploading the Code vectorized backtesting, Python Scripts-Mean Reversion Backtesting Class Q Quandlpremium data sets, Working with Open Data Sources working with open data sources, Working with Open Data Sources-Working with Open Data Sources R random numbers, Random Numbers random walk hypothesis, Predicting Index Levels range (iterator object), Control Structures read_csv() function, Reading from a CSV File with pandas real-time data, Working with Real-Time Data and Sockets-Sample Data Server for Bar PlotPython script for handling, Python Scripts-Sample Data Server for Bar Plot signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time tick data client for, Connecting a Simple Tick Data Client tick data server for, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server visualizing streaming data with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars real-time monitoring, Real-Time Monitoring Refinitiv, Eikon Data API relative maximum drawdown, Case Study returns, predicting future, Predicting Future Returns-Predicting Future Returns risk analysis, for ML-based trading strategy, Risk Analysis-Risk Analysis RSA public/private keys, RSA Public and Private Keys .run_mean_reversion_strategy() method, Long-Only Backtesting Class, Long-Short Backtesting Class .run_simulation() method, Kelly Criterion in Binomial Setting S S&P 500, Algorithmic Trading-Algorithmic Tradinglogistic regression-based strategies and, Generalizing the Approach momentum strategies, Getting into the Basics passive long position in, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices scatter objects, Three Real-Time Streams scientific stack, NumPy and Vectorization, Python, NumPy, matplotlib, pandas scikit-learn, Linear Regression with scikit-learn ScikitBacktester class, Generalizing the Approach-Generalizing the Approach SciPy package project, NumPy and Vectorization seaborn library, matplotlib-matplotlib simple moving averages (SMAs), pandas and the DataFrame Class, Simple Moving Averagestrading strategies based on, Strategies Based on Simple Moving Averages-Generalizing the Approach visualization with price ticks, Three Real-Time Streams .simulate_value() method, Running a Simple Tick Data Server Singer, Paul, CFD Trading with Oanda sockets, real-time data and, Working with Real-Time Data and Sockets-Sample Data Server for Bar Plot sorting list objects, Data Structures SQLite3, Storing Data with SQLite3-Storing Data with SQLite3 SSL certificate, RSA Public and Private Keys storage (see data storage) streaming bar plots, Streaming Data as Bars, Sample Data Server for Bar Plot streaming dataOanda and, Working with Streaming Data visualization with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars string objects (str), Data Types-Data Types Swiss Franc event, CFD Trading with Oanda systematic macro hedge funds, Algorithmic Trading T TensorFlow, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction Thomas, Rob, Working with Financial Data Thorp, Edward, Capital Management tick data client, Connecting a Simple Tick Data Client tick data server, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server time series data setspandas and vectorization, Vectorization with pandas price prediction based on, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction Python script for generating sample set, Python Scripts SQLite3 for storage of, Storing Data with SQLite3-Storing Data with SQLite3 TsTables for storing, Using TsTables-Using TsTables time series momentum strategies, Strategies Based on Momentum(see also momentum strategies) .to_hdf() method, Storing DataFrame Objects tpqoa wrapper package, The Oanda API, Working with Streaming Data trading platforms, factors influencing choice of, CFD Trading with Oanda trading strategies, Trading Strategies-Conclusions(see also specific strategies) implementing in real time with Oanda, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time machine learning/deep learning, Machine and Deep Learning mean-reversion, NumPy and Vectorization momentum, Momentum simple moving averages, Simple Moving Averages trading, motives for, Algorithmic Trading transaction costs, Long-Only Backtesting Class, Vectorized Backtesting TsTables package, Using TsTables-Using TsTables tuple objects, Data Structures U Ubuntu, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image universal functions, NumPy, ndarray Methods and NumPy Functions V v20 wrapper package, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting value-at-risk (VAR), Risk Analysis-Risk Analysis vectorization, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approach vectorized backtestingdata snooping and overfitting, Data Snooping and Overfitting-Conclusions ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting momentum-based trading strategies, Strategies Based on Momentum-Generalizing the Approach potential shortcomings, Building Classes for Event-Based Backtesting Python code with a class for vectorized backtesting of mean-reversion trading strategies, Momentum Backtesting Class Python scripts for, Python Scripts-Mean Reversion Backtesting Class, Linear Regression Backtesting Class regression-based strategy, Vectorized Backtesting of Regression-Based Strategy trading strategies based on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach vectorization with NumPy, Vectorization with NumPy-Vectorization with NumPy vectorization with pandas, Vectorization with pandas-Vectorization with pandas vectorized operations, Vectorized Operations virtual environment management, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager W while loops, Control Structures Z ZeroMQ, Working with Real-Time Data and Sockets About the Author Dr.

Index A absolute maximum drawdown, Case Study AdaBoost algorithm, Vectorized Backtesting addition (+) operator, Data Types adjusted return appraisal ratio, Algorithmic Trading algorithmic trading (generally)advantages of, Algorithmic Trading basics, Algorithmic Trading-Algorithmic Trading strategies, Trading Strategies-Conclusions alpha seeking strategies, Trading Strategies alpha, defined, Algorithmic Trading anonymous functions, Python Idioms API key, for data sets, Working with Open Data Sources-Working with Open Data Sources Apple, Inc.intraday stock prices, Getting into the Basics reading stock price data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON retrieving historical unstructured data about, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data app_key, for Eikon Data API, Eikon Data API AQR Capital Management, pandas and the DataFrame Class arithmetic operations, Data Types array programming, Making Use of Vectorization(see also vectorization) automated trading operations, Automating Trading Operations-Strategy Monitoringcapital management, Capital Management-Kelly Criterion for Stocks and Indices configuring Oanda account, Configuring Oanda Account hardware setup, Setting Up the Hardware infrastructure and deployment, Infrastructure and Deployment logging and monitoring, Logging and Monitoring-Logging and Monitoring ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object online algorithm, Online Algorithm-Online Algorithm Python environment setup, Setting Up the Python Environment Python scripts for, Python Script-Strategy Monitoring real-time monitoring, Real-Time Monitoring running code, Running the Code uploading code, Uploading the Code visual step-by-step overview, Visual Step-by-Step Overview-Real-Time Monitoring B backtestingbased on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach Python scripts for classification algorithm backtesting, Classification Algorithm Backtesting Class Python scripts for linear regression backtesting class, Linear Regression Backtesting Class vectorized (see vectorized backtesting) BacktestLongShort class, Long-Short Backtesting Class, Long-Short Backtesting Class bar charts, matplotlib bar plots (see Plotly; streaming bar plot) base class, for event-based backtesting, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class Bash script, Building a Ubuntu and Python Docker Imagefor Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up for Python/Jupyter Lab installation, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Bitcoin, pandas and the DataFrame Class, Working with Open Data Sources Boolean operationsNumPy, Boolean Operations pandas, Boolean Operations C callback functions, Retrieving Streaming Data capital managementautomated trading operations and, Capital Management-Kelly Criterion for Stocks and Indices Kelly criterion for stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Kelly criterion in binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting Carter, Graydon, FX Trading with FXCM CFD (contracts for difference)algorithmic trading risks, Logging and Monitoring defined, CFD Trading with Oanda risks of losses, Long-Short Backtesting Class risks of trading on margin, FX Trading with FXCM trading with Oanda, CFD Trading with Oanda-Python Script(see also Oanda) classification problemsmachine learning for, A Simple Classification Problem-A Simple Classification Problem neural networks for, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited Python scripts for vectorized backtesting, Classification Algorithm Backtesting Class .close_all() method, Placing Orders cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Upinstallation script for Python and Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Jupyter Notebook configuration file, Jupyter Notebook Configuration File RSA public/private keys, RSA Public and Private Keys script to orchestrate Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up Cocteau, Jean, Building Classes for Event-Based Backtesting comma separated value (CSV) files (see CSV files) condaas package manager, Conda as a Package Manager-Basic Operations with Conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager basic operations, Basic Operations with Conda-Basic Operations with Conda installing Miniconda, Installing Miniconda-Installing Miniconda conda remove, Basic Operations with Conda configparser module, The Oanda API containers (see Docker containers) contracts for difference (see CFD) control structures, Control Structures CPython, Python for Finance, Python Infrastructure .create_market_buy_order() method, Placing Orders .create_order() method, Placing Market Orders-Placing Market Orders cross-sectional momentum strategies, Strategies Based on Momentum CSV filesinput-output operations, Input-Output Operations-Input-Output Operations reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python .cummax() method, Case Study currency pairs, Logging and Monitoring(see also EUR/USD exchange rate) algorithmic trading risks, Logging and Monitoring D data science stack, Python, NumPy, matplotlib, pandas data snooping, Data Snooping and Overfitting data storageSQLite3 for, Storing Data with SQLite3-Storing Data with SQLite3 storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects TsTables package for, Using TsTables-Using TsTables data structures, Data Structures-Data Structures DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class, Reading from a CSV File with pandas, DataFrame Class-DataFrame Class DataFrame objectscreating, Vectorization with pandas storing, Storing DataFrame Objects-Storing DataFrame Objects dataism, Preface DatetimeIndex() constructor, Plotting with pandas decision tree classification algorithm, Vectorized Backtesting deep learningadding features to analysis, Adding Different Types of Features-Adding Different Types of Features classification problem, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited deep neural networks for predicting market direction, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features market movement prediction, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features trading strategies and, Machine and Deep Learning deep neural networks, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features delta hedging, Algorithmic Trading dense neural network (DNN), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction dictionary (dict) objects, Reading from a CSV File with Python, Data Structures DigitalOceancloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up droplet setup, Setting Up the Hardware DNN (dense neural network), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Imagebuilding a Ubuntu and Python Docker image, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image defined, Docker Images and Containers Docker images versus, Docker Images and Containers Docker imagesdefined, Docker Images and Containers Docker containers versus, Docker Images and Containers Dockerfile, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image Domingos, Pedro, Automating Trading Operations Droplet, Using Cloud Instancescosts, Infrastructure and Deployment script to orchestrate set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up dynamic hedging, Algorithmic Trading E efficient market hypothesis, Predicting Market Movements with Machine Learning Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Dataretrieving historical structured data, Retrieving Historical Structured Data-Retrieving Historical Structured Data retrieving historical unstructured data, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data Euler discretization, Python Versus Pseudo-Code EUR/USD exchange ratebacktesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars evaluation of regression-based strategy, Generalizing the Approach factoring in leverage/margin, Factoring In Leverage and Margin-Factoring In Leverage and Margin gross performance versus deep learning-based strategy, Using Deep Neural Networks to Predict Market Direction-Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features-Adding Different Types of Features historical ask close prices, Retrieving Historical Data-Retrieving Historical Data historical candles data for, Retrieving Candles Data historical tick data for, Retrieving Tick Data implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time logistic regression-based strategies, Generalizing the Approach placing orders, Placing Orders-Placing Orders predicting, Predicting Index Levels-Predicting Index Levels predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels retrieving streaming data for, Retrieving Streaming Data retrieving trading account information, Retrieving Account Information-Retrieving Account Information SMA calculation, Getting into the Basics-Generalizing the Approach vectorized backtesting of ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy event-based backtesting, Building Classes for Event-Based Backtesting-Long-Short Backtesting Classadvantages, Building Classes for Event-Based Backtesting base class, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class building classes for, Building Classes for Event-Based Backtesting-Long-Short Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class Python scripts for, Backtesting Base Class-Long-Short Backtesting Class Excelexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON F featuresadding different types, Adding Different Types of Features-Adding Different Types of Features lags and, Using Logistic Regression to Predict Market Direction financial data, working with, Working with Financial Data-Python Scriptsdata set for examples, The Data Set Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Data exporting to Excel/JSON, Exporting to Excel and JSON open data sources, Working with Open Data Sources-Working with Open Data Sources reading data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON reading data from Excel/JSON, Reading from Excel and JSON reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 .flatten() method, matplotlib foreign exchange trading (see FX trading; FXCM) future returns, predicting, Predicting Future Returns-Predicting Future Returns FX trading, FX Trading with FXCM-References and Further Resources(see also EUR/USD exchange rate) FXCMFX trading, FX Trading with FXCM-References and Further Resources getting started, Getting Started placing orders, Placing Orders-Placing Orders retrieving account information, Account Information retrieving candles data, Retrieving Candles Data-Retrieving Candles Data retrieving data, Retrieving Data-Retrieving Candles Data retrieving historical data, Retrieving Historical Data-Retrieving Historical Data retrieving streaming data, Retrieving Streaming Data retrieving tick data, Retrieving Tick Data-Retrieving Tick Data working with the API, Working with the API-Account Information fxcmpy wrapper packagecallback functions, Retrieving Streaming Data installing, Getting Started tick data retrieval, Retrieving Tick Data fxTrade, CFD Trading with Oanda G GDX (VanEck Vectors Gold Miners ETF)logistic regression-based strategies, Generalizing the Approach mean-reversion strategies, Getting into the Basics-Generalizing the Approach regression-based strategies, Generalizing the Approach generate_sample_data(), Storing Financial Data Efficiently .get_account_summary() method, Retrieving Account Information .get_candles() method, Retrieving Historical Data .get_data() method, Backtesting Base Class, Retrieving Tick Data .get_date_price() method, Backtesting Base Class .get_instruments() method, Looking Up Instruments Available for Trading .get_last_price() method, Retrieving Streaming Data .get_raw_data() method, Retrieving Tick Data get_timeseries() function, Retrieving Historical Structured Data .get_transactions() method, Retrieving Account Information GLD (SPDR Gold Shares)logistic regression-based strategies, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction mean-reversion strategies, Getting into the Basics-Generalizing the Approach gold pricemean-reversion strategies, Getting into the Basics-Getting into the Basics momentum strategy and, Getting into the Basics-Getting into the Basics, Generalizing the Approach-Generalizing the Approach Goldman Sachs, Python and Algorithmic Trading, Algorithmic Trading .go_long() method, Long-Short Backtesting Class H half Kelly criterion, Optimal Leverage Harari, Yuval Noah, Preface HDF5 binary storage library, Using TsTables-Using TsTables HDFStore wrapper, Storing DataFrame Objects-Storing DataFrame Objects high frequency trading (HFQ), Algorithmic Trading histograms, matplotlib hit ratio, defined, Vectorized Backtesting I if-elif-else control structure, Python Idioms in-sample fitting, Generalizing the Approach index levels, predicting, Predicting Index Levels-Predicting Index Levels infrastructure (see Python infrastructure) installation script, Python/Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Intel Math Kernel Library, Basic Operations with Conda iterations, Control Structures J JSONexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON Jupyter Labinstallation script for, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab RSA public/private keys for, RSA Public and Private Keys tools included, Using Cloud Instances Jupyter Notebook, Jupyter Notebook Configuration File K Kelly criterionin binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting optimal leverage, Optimal Leverage-Optimal Leverage stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Keras, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features key-value stores, Data Structures keys, public/private, RSA Public and Private Keys L lags, The Basic Idea for Price Prediction, Using Logistic Regression to Predict Market Direction lambda functions, Python Idioms LaTeX, Python Versus Pseudo-Code leveraged trading, risks of, Factoring In Leverage and Margin, FX Trading with FXCM, Optimal Leverage linear regressiongeneralizing the approach, Generalizing the Approach market movement prediction, Using Linear Regression for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction review of, A Quick Review of Linear Regression scikit-learn and, Linear Regression with scikit-learn vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy, Linear Regression Backtesting Class list comprehension, Python Idioms list constructor, Data Structures list objects, Reading from a CSV File with Python, Data Structures, Regular ndarray Object logging, of automated trading operations, Logging and Monitoring-Logging and Monitoring logistic regressiongeneralizing the approach, Generalizing the Approach-Generalizing the Approach market direction prediction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction Python script for vectorized backtesting, Classification Algorithm Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class longest drawdown period, Risk Analysis M machine learningclassification problem, A Simple Classification Problem-A Simple Classification Problem linear regression with scikit-learn, Linear Regression with scikit-learn market movement prediction, Using Machine Learning for Market Movement Prediction-Generalizing the Approach ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object Python scripts, Linear Regression Backtesting Class trading strategies and, Machine and Deep Learning using logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction macro hedge funds, algorithmic trading and, Algorithmic Trading __main__ method, Backtesting Base Class margin trading, FX Trading with FXCM market direction prediction, Predicting Future Market Direction market movement predictiondeep learning for, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features deep neural networks for, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features linear regression for, Using Linear Regression for Market Movement Prediction-Generalizing the Approach linear regression with scikit-learn, Linear Regression with scikit-learn logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction machine learning for, Using Machine Learning for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy market orders, placing, Placing Market Orders-Placing Market Orders math module, Data Types mathematical functions, Data Types matplotlib, matplotlib-matplotlib, Plotting with pandas-Plotting with pandas maximum drawdown, Risk Analysis, Case Study McKinney, Wes, pandas and the DataFrame Class mean-reversion strategies, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approachbasics, Getting into the Basics-Generalizing the Approach generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Miniconda, Installing Miniconda-Installing Miniconda mkl (Intel Math Kernel Library), Basic Operations with Conda ML-based strategies, ML-Based Trading Strategy-Persisting the Model Objectoptimal leverage, Optimal Leverage-Optimal Leverage persisting the model object, Persisting the Model Object Python script for, Automated Trading Strategy risk analysis, Risk Analysis-Risk Analysis vectorized backtesting, Vectorized Backtesting-Vectorized Backtesting MLPClassifier, The Simple Classification Problem Revisited MLTrader class, Online Algorithm-Online Algorithm momentum strategies, Momentumbacktesting on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars basics, Getting into the Basics-Getting into the Basics generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Python script for custom streaming class, Python Script Python script for momentum online algorithm, Momentum Online Algorithm vectorized backtesting of, Strategies Based on Momentum-Generalizing the Approach MomentumTrader class, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time MomVectorBacktester class, Generalizing the Approach monitoringautomated trading operations, Logging and Monitoring-Logging and Monitoring, Real-Time Monitoring Python scripts for strategy monitoring, Strategy Monitoring Monte Carlo simulationsample tick data server, Sample Tick Data Server time series data based on, Python Scripts motives, for trading, Algorithmic Trading MRVectorBacktester class, Generalizing the Approach multi-layer perceptron, The Simple Classification Problem Revisited Musashi, Miyamoto, Python Infrastructure N natural language processing (NLP), Retrieving Historical Unstructured Data ndarray class, Vectorization with NumPy-Vectorization with NumPy ndarray objects, NumPy and Vectorization, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functionscreating, ndarray Creation linear regression and, A Quick Review of Linear Regression regular, Regular ndarray Object nested structures, Data Structures NLP (natural language processing), Retrieving Historical Unstructured Data np.arange(), ndarray Creation numbers, data typing of, Data Types numerical operations, pandas, Numerical Operations NumPy, NumPy and Vectorization-NumPy and Vectorization, NumPy-Random NumbersBoolean operations, Boolean Operations ndarray creation, ndarray Creation ndarray methods, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functions random numbers, Random Numbers regular ndarray object, Regular ndarray Object universal functions, ndarray Methods and NumPy Functions vectorization, Vectorization with NumPy-Vectorization with NumPy vectorized operations, Vectorized Operations numpy.random sub-package, Random Numbers NYSE Arca Gold Miners Index, Getting into the Basics O Oandaaccount configuration, Configuring Oanda Account account setup, Setting Up an Account API access, The Oanda API-The Oanda API backtesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars CFD trading, CFD Trading with Oanda-Python Script factoring in leverage/margin with historical data, Factoring In Leverage and Margin-Factoring In Leverage and Margin implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time looking up instruments available for trading, Looking Up Instruments Available for Trading placing market orders, Placing Market Orders-Placing Market Orders Python script for custom streaming class, Python Script retrieving account information, Retrieving Account Information-Retrieving Account Information retrieving historical data, Retrieving Historical Data-Factoring In Leverage and Margin working with streaming data, Working with Streaming Data Oanda v20 RESTful API, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting offline algorithmdefined, Signal Generation in Real Time transformation to online algorithm, Online Algorithm OLS (ordinary least squares) regression, matplotlib online algorithmautomated trading operations, Online Algorithm-Online Algorithm defined, Signal Generation in Real Time Python script for momentum online algorithm, Momentum Online Algorithm signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time transformation of offline algorithm to, Online Algorithm .on_success() method, Implementing Trading Strategies in Real Time, Online Algorithm open data sources, Working with Open Data Sources-Working with Open Data Sources ordinary least squares (OLS) regression, matplotlib out-of-sample evaluation, Generalizing the Approach overfitting, Data Snooping and Overfitting P package manager, conda as, Conda as a Package Manager-Basic Operations with Conda pandas, pandas and the DataFrame Class-pandas and the DataFrame Class, pandas-Input-Output OperationsBoolean operations, Boolean Operations case study, Case Study-Case Study data selection, Data Selection-Data Selection DataFrame class, DataFrame Class-DataFrame Class exporting financial data to Excel/JSON, Exporting to Excel and JSON input-output operations, Input-Output Operations-Input-Output Operations numerical operations, Numerical Operations plotting, Plotting with pandas-Plotting with pandas reading financial data from Excel/JSON, Reading from Excel and JSON reading from a CSV file, Reading from a CSV File with pandas storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects vectorization, Vectorization with pandas-Vectorization with pandas password protection, for Jupyter lab, Jupyter Notebook Configuration File .place_buy_order() method, Backtesting Base Class .place_sell_order() method, Backtesting Base Class Plotlybasics, The Basics multiple real-time streams for, Three Real-Time Streams multiple sub-plots for streams, Three Sub-Plots for Three Streams streaming data as bars, Streaming Data as Bars visualization of streaming data, Visualizing Streaming Data with Plotly-Streaming Data as Bars plotting, with pandas, Plotting with pandas-Plotting with pandas .plot_data() method, Backtesting Base Class polyfit()/polyval() convenience functions, matplotlib price prediction, based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction .print_balance() method, Backtesting Base Class .print_net_wealth() method, Backtesting Base Class .print_transactions() method, Retrieving Account Information pseudo-code, Python versus, Python Versus Pseudo-Code publisher-subscriber (PUB-SUB) pattern, Working with Real-Time Data and Sockets Python (generally)advantages of, Python for Algorithmic Trading basics, Python and Algorithmic Trading-References and Further Resources control structures, Control Structures data structures, Data Structures-Data Structures data types, Data Types-Data Types deployment difficulties, Python Infrastructure idioms, Python Idioms-Python Idioms NumPy and vectorization, NumPy and Vectorization-NumPy and Vectorization obstacles to adoption in financial industry, Python for Finance origins, Python for Finance pandas and DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class pseudo-code versus, Python Versus Pseudo-Code reading from a CSV file, Reading from a CSV File with Python-Reading from a CSV File with Python Python infrastructure, Python Infrastructure-References and Further Resourcesconda as package manager, Conda as a Package Manager-Basic Operations with Conda conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Image using cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up Python scriptsautomated trading operations, Running the Code, Python Script-Strategy Monitoring backtesting base class, Backtesting Base Class custom streaming class that trades a momentum strategy, Python Script linear regression backtesting class, Linear Regression Backtesting Class long-only backtesting class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class real-time data handling, Python Scripts-Sample Data Server for Bar Plot sample time series data set, Python Scripts strategy monitoring, Strategy Monitoring uploading for automated trading operations, Uploading the Code vectorized backtesting, Python Scripts-Mean Reversion Backtesting Class Q Quandlpremium data sets, Working with Open Data Sources working with open data sources, Working with Open Data Sources-Working with Open Data Sources R random numbers, Random Numbers random walk hypothesis, Predicting Index Levels range (iterator object), Control Structures read_csv() function, Reading from a CSV File with pandas real-time data, Working with Real-Time Data and Sockets-Sample Data Server for Bar PlotPython script for handling, Python Scripts-Sample Data Server for Bar Plot signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time tick data client for, Connecting a Simple Tick Data Client tick data server for, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server visualizing streaming data with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars real-time monitoring, Real-Time Monitoring Refinitiv, Eikon Data API relative maximum drawdown, Case Study returns, predicting future, Predicting Future Returns-Predicting Future Returns risk analysis, for ML-based trading strategy, Risk Analysis-Risk Analysis RSA public/private keys, RSA Public and Private Keys .run_mean_reversion_strategy() method, Long-Only Backtesting Class, Long-Short Backtesting Class .run_simulation() method, Kelly Criterion in Binomial Setting S S&P 500, Algorithmic Trading-Algorithmic Tradinglogistic regression-based strategies and, Generalizing the Approach momentum strategies, Getting into the Basics passive long position in, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices scatter objects, Three Real-Time Streams scientific stack, NumPy and Vectorization, Python, NumPy, matplotlib, pandas scikit-learn, Linear Regression with scikit-learn ScikitBacktester class, Generalizing the Approach-Generalizing the Approach SciPy package project, NumPy and Vectorization seaborn library, matplotlib-matplotlib simple moving averages (SMAs), pandas and the DataFrame Class, Simple Moving Averagestrading strategies based on, Strategies Based on Simple Moving Averages-Generalizing the Approach visualization with price ticks, Three Real-Time Streams .simulate_value() method, Running a Simple Tick Data Server Singer, Paul, CFD Trading with Oanda sockets, real-time data and, Working with Real-Time Data and Sockets-Sample Data Server for Bar Plot sorting list objects, Data Structures SQLite3, Storing Data with SQLite3-Storing Data with SQLite3 SSL certificate, RSA Public and Private Keys storage (see data storage) streaming bar plots, Streaming Data as Bars, Sample Data Server for Bar Plot streaming dataOanda and, Working with Streaming Data visualization with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars string objects (str), Data Types-Data Types Swiss Franc event, CFD Trading with Oanda systematic macro hedge funds, Algorithmic Trading T TensorFlow, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction Thomas, Rob, Working with Financial Data Thorp, Edward, Capital Management tick data client, Connecting a Simple Tick Data Client tick data server, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server time series data setspandas and vectorization, Vectorization with pandas price prediction based on, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction Python script for generating sample set, Python Scripts SQLite3 for storage of, Storing Data with SQLite3-Storing Data with SQLite3 TsTables for storing, Using TsTables-Using TsTables time series momentum strategies, Strategies Based on Momentum(see also momentum strategies) .to_hdf() method, Storing DataFrame Objects tpqoa wrapper package, The Oanda API, Working with Streaming Data trading platforms, factors influencing choice of, CFD Trading with Oanda trading strategies, Trading Strategies-Conclusions(see also specific strategies) implementing in real time with Oanda, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time machine learning/deep learning, Machine and Deep Learning mean-reversion, NumPy and Vectorization momentum, Momentum simple moving averages, Simple Moving Averages trading, motives for, Algorithmic Trading transaction costs, Long-Only Backtesting Class, Vectorized Backtesting TsTables package, Using TsTables-Using TsTables tuple objects, Data Structures U Ubuntu, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image universal functions, NumPy, ndarray Methods and NumPy Functions V v20 wrapper package, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting value-at-risk (VAR), Risk Analysis-Risk Analysis vectorization, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approach vectorized backtestingdata snooping and overfitting, Data Snooping and Overfitting-Conclusions ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting momentum-based trading strategies, Strategies Based on Momentum-Generalizing the Approach potential shortcomings, Building Classes for Event-Based Backtesting Python code with a class for vectorized backtesting of mean-reversion trading strategies, Momentum Backtesting Class Python scripts for, Python Scripts-Mean Reversion Backtesting Class, Linear Regression Backtesting Class regression-based strategy, Vectorized Backtesting of Regression-Based Strategy trading strategies based on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach vectorization with NumPy, Vectorization with NumPy-Vectorization with NumPy vectorization with pandas, Vectorization with pandas-Vectorization with pandas vectorized operations, Vectorized Operations virtual environment management, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager W while loops, Control Structures Z ZeroMQ, Working with Real-Time Data and Sockets About the Author Dr.


pages: 589 words: 147,053

The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business cycle, business process, Clayton Christensen, cloud computing, correlation does not imply causation, creative destruction, deep learning, demographic transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, Future Shock, Herman Kahn, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, megaproject, meta-analysis, Nash equilibrium, new economy, Nick Bostrom, pneumatic tube, power law, prediction markets, quantum cryptography, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, social distancing, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Tyler Cowen, Vernor Vinge, William MacAskill

Combinatorial versions can even allow a small number of users to manage billions of consistent interconnected estimates, so that updates on some topics automatically improve the accuracy of estimates of quite different topics (Sun et al. 2012). Head-to-head comparisons between prediction markets and other forecasting mechanisms, given similar resources on the same questions, find prediction markets to be consistently either about as accurate, or substantially more accurate than, other mechanisms. Compared with other mechanisms, prediction markets are more robust to situations where no one knows anything useful, where most invited participants are ignorant or fools, and where some participants are willing to lie or lose money to distort resulting estimates. Prediction markets can tell a firm how likely a project is to make its deadline, how likely a supplier is to deliver as promised, or how many units of a particular product will be sold in a particular region.

As better auction mechanisms could plausibly deliver great value, and as the relevant research community has well-established ways to develop better mechanisms, it seems safe to guess that such mechanisms will be available if desired. Prediction Markets I have also been personally involved in developing the new institution of “prediction markets.” These are variations on speculative and betting markets that can encourage and facilitate the aggregation of information on important outcomes. By subsidizing trading on particular questions of interest, one can induce people able to learn about those questions to self-select into improving visible consensus estimates (Hanson 2003). Prediction markets give clear precise continually updated estimates that are consistent across many topics.

To avoid this problem, an independent (and hard to hurt) judge might also join the speaker and listener in the safe, with the power to declare the safe “void” if they heard sufficient indications of such threats. To reduce the cost of using these safes, an em might offer to let a wider audience make unlimited bets on what the safe would produce if created, but only actually create the safe a small fraction, for example, 1%, of the time. (More on such prediction markets in Chapter 15, Prediction Markets section.) Such offers signal loyalty, showing that one trusted the listener spur to evaluate one’s argument fairly once inside the safe. Because of safes, on important questions ems rarely need to just accept another em’s claim that they have good reasons for believing something but can’t explain those reasons because of a need for secrecy.


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

"World Economic Forum" Davos, Airbnb, altcoin, Alvin Toffler, asset-backed security, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, business logic, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, commons-based peer production, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, currency risk, decentralized internet, digital capitalism, disintermediation, disruptive innovation, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Future Shock, Galaxy Zoo, general purpose technology, George Gilder, glass ceiling, Google bus, GPS: selective availability, Hacker News, Hernando de Soto, Higgs boson, holacracy, income inequality, independent contractor, informal economy, information asymmetry, information security, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Neal Stephenson, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, quantitative easing, radical decentralization, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, search costs, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, Snow Crash, social graph, social intelligence, social software, standardized shipping container, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, systems thinking, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Soul of a New Machine, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Tyler Cowen, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, vertical integration, Vitalik Buterin, wealth creators, X Prize, Y2K, Yochai Benkler, Zipcar

By harnessing the wisdom of the crowds, we can form more realistic predictions of the future, leading to more efficient markets. Prediction markets can serve as a hedge against global uncertainty and “black swan” events: “Will Greece’s economy shrink by more than 15 percent this year?”84 Today, we rely on a few talking heads to sound the alarms; a prediction market would act more impartially as an early warning system for investors globally. Prediction markets could complement and ultimately transform many aspects of the financial system. Consider prediction markets on the outcomes of corporate actions—earnings reports, mergers, acquisitions, and changes in management. Prediction markets would inform the insurance of value and the hedging of risk, potentially even displacing esoteric financial instruments like options, interest rate swaps and credit default swaps.

The NYSE invested in Coinbase and NASDAQ is integrating blockchain technology into its private market. Bob Greifeld, CEO of NASDAQ, is starting small, using blockchain to “streamline financial record keeping while making it cheaper and more accurate,”81 but evidently NASDAQ and other incumbents have bigger plans. THE MARKET FOR PREDICTION MARKETS Augur is building a decentralized prediction market platform that rewards users for correctly predicting future events—sporting events, election results, new product launches, the genders of celebrity babies. How does it work? Augur users can purchase or sell shares in the outcome of a future event, the value of which is an estimate of the probability of an event happening.

Maintaining the integrity of the system has other monetary benefits: the more reputation points you have, the more markets you can make, and thus the more fees you can charge. In Augur’s words, “our prediction markets eliminate counterparty risks, centralized servers, and create a global market by employing cryptocurrencies including bitcoin, ether, and stable cryptocurrencies. All funds are stored in smart contracts, and no one can steal the money.”83 Augur resolves the issue of unethical contracts by having a zero-tolerance policy for crime. To Augur’s leadership team, human imagination is the only practical limit to the utility of prediction markets. On Augur, anyone can post a clearly defined prediction about anything with a clear end date—from the trivial, “Will Brad Pitt and Angelina Jolie divorce?”


pages: 267 words: 71,941

How to Predict the Unpredictable by William Poundstone

accounting loophole / creative accounting, Albert Einstein, Bernie Madoff, Brownian motion, business cycle, butter production in bangladesh, buy and hold, buy low sell high, call centre, centre right, Claude Shannon: information theory, computer age, crowdsourcing, Daniel Kahneman / Amos Tversky, Edward Thorp, Firefox, fixed income, forensic accounting, high net worth, index card, index fund, Jim Simons, John von Neumann, market bubble, money market fund, pattern recognition, Paul Samuelson, Ponzi scheme, power law, prediction markets, proprietary trading, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Rubik’s Cube, statistical model, Steven Pinker, subprime mortgage crisis, transaction costs

More to the point, there are accurate prediction markets for Oscars. In sports, prediction markets are hampered by the games’ built-in randomness. Look at the shape of a football. The way it bounces can be as capricious as a coin toss. Sometimes that bounce decides a close game. The crowd can’t anticipate that, no matter how wise they may be about the teams’ overall strengths. Prediction markets are good at aggregating what the crowd already knows. That describes the Oscars, where the votes reflect opinions that Academy members have held for weeks or months. Oscar prediction markets are not forecasting the future but deducing a poorly kept secret.

A third category of player is the hard-core strategist who consults the prediction markets. This is the type you have to worry about. Should you believe that all the other players are Oscar buzz–deficient, you’d do best by going with the straight prediction market picks. These choices have the best chance of being right. The strategy needs to be adjusted when there are other well-informed players in the pool. In that case, you may want to choose a contrarian pick. That will usually be a second-place choice, going by the prediction market odds. Should you luck out and be correct, you would leap ahead of the pack of informed voters.

The media often refer to point-spread betting as a prediction market, a term that has lately taken on an aura of infallibility. A spread of five points supposedly means that the median bettor believes the favorite will beat the underdog by five points. It’s the crowd’s prediction. This is not quite true, and it’s important to understand why. In 2004 economist Steven Levitt (also known as the coauthor of Freakonomics) advanced the theory that bookies set the point spread to maximize their profits—that, rather than running a fancy prediction market. They don’t always balance bets exactly, and the point spread might not reflect the crowd’s average opinion.


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, behavioural economics, Black Swan, butterfly effect, buy and hold, cloud computing, cognitive load, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, How many piano tuners are there in Chicago?, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, operational security, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, precautionary principle, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, tacit knowledge, tail risk, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

By aggregating all these judgments, the contract price should, in theory, closely track the true probability of Hillary Clinton winning. Prediction markets like the famous Iowa Electronic Markets have an impressive track record. And they have a theory, backed by a battalion of Nobel laureates, going for them. So who would win in a battle between superteams and prediction markets? Most economists would say it’s no contest. Prediction markets would mop the floor with the superteams. We put that proposition to the test by randomly assigning regular forecasters to one of three experimental conditions. Some worked alone. Others worked in teams. And some were traders in prediction markets run by companies such as Inkling and Lumenogic.

Of course, after year 1—when the value of teams was resoundingly demonstrated—nobody expected forecasters working alone to compete at the level of teams or prediction markets, so we combined all their forecasts and calculated the unweighted average to get the “wisdom of the crowd.” And of course we had one more competitor: superteams. The results were clear-cut each year. Teams of ordinary forecasters beat the wisdom of the crowd by about 10%. Prediction markets beat ordinary teams by about 20%. And superteams beat prediction markets by 15% to 30%. I can already hear the protests from my colleagues in finance that the only reason the superteams beat the prediction markets was that our markets lacked liquidity: real money wasn’t at stake and we didn’t have a critical mass of traders.

I can already hear the protests from my colleagues in finance that the only reason the superteams beat the prediction markets was that our markets lacked liquidity: real money wasn’t at stake and we didn’t have a critical mass of traders. They may be right. It is a testable idea, and one worth testing. It’s also important to recognize that while superteams beat prediction markets, prediction markets did a pretty good job of forecasting complex global events. How did superteams do so well? By avoiding the extremes of groupthink and Internet flame wars. And by fostering minicultures that encouraged people to challenge each other respectfully, admit ignorance, and request help. In key ways, superteams resembled the best surgical teams identified by Harvard’s Amy Edmondson, in which the nurse doesn’t hesitate to tell the surgeon he left a sponge behind the pancreas because she knows it is “psychologically safe” to correct higher-ups.


pages: 344 words: 104,077

Superminds: The Surprising Power of People and Computers Thinking Together by Thomas W. Malone

Abraham Maslow, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, Boeing 747, business process, call centre, carbon tax, clean water, Computing Machinery and Intelligence, creative destruction, crowdsourcing, data science, deep learning, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, Ford Model T, future of work, Future Shock, Galaxy Zoo, Garrett Hardin, gig economy, happiness index / gross national happiness, independent contractor, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, machine translation, Marshall McLuhan, Nick Bostrom, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, radical decentralization, Ray Kurzweil, Rodney Brooks, Ronald Coase, search costs, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Tragedy of the Commons, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

This combination of diverse approaches resulted in overall predictions that were better than either the people or computers made alone. It’s easy to imagine that cyber-human prediction markets like this could be used in many ways. Google and Microsoft, for example, have already let their employees use prediction markets to estimate completion dates for internal projects. The University of Iowa has been using prediction markets to predict the winners of US presidential races for decades. The Hollywood Stock Exchange website uses prediction markets to predict movie box-office receipts. In all these cases, the predictions from the prediction markets are usually as good as or better than any alternative prediction methods.

Here’s how it worked: We showed groups of people videos of a football game, and just before each play began, we stopped the videos and asked people to predict whether the play would be a run or a pass. Rather than asking our subjects to just make a simple prediction, we asked them to express their predictions by participating in a prediction market. Somewhat like futures markets, prediction markets let you buy and sell “shares” of predictions about possible future events. For instance, if you think the next play will very likely be a pass, you should buy shares of this prediction. If your predictions are right, then you (typically) get one dollar for each share you own, and if your predictions are wrong you get nothing.5 But the market lets you express your opinions even more precisely than that.

The upshot here is twofold: providing an incentive for bot designers to make increasingly smarter bots will lead to advances in AI, and the prediction markets in which those bots participate will become more accurate. It’s important to note, by the way, that this approach can be useful with the artificial intelligence that we have today. In fact, it can even work with simple prediction algorithms, like statistical regression, that have existed for decades. Cyber-Human Markets for Everything Most of what we’ve just been saying applies to far more than just prediction markets; it applies to many other kinds of markets, too. Today’s financial markets are leading the way, with investment managers increasingly relying on quantitative, often AI-based, trading algorithms.


pages: 256 words: 60,620

Think Twice: Harnessing the Power of Counterintuition by Michael J. Mauboussin

affirmative action, Alan Greenspan, asset allocation, Atul Gawande, availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, butter production in bangladesh, Cass Sunstein, choice architecture, Clayton Christensen, cognitive dissonance, collateralized debt obligation, Daniel Kahneman / Amos Tversky, deliberate practice, disruptive innovation, Edward Thorp, experimental economics, financial engineering, financial innovation, framing effect, fundamental attribution error, Geoffrey West, Santa Fe Institute, George Akerlof, hindsight bias, hiring and firing, information asymmetry, libertarian paternalism, Long Term Capital Management, loose coupling, loss aversion, mandelbrot fractal, Menlo Park, meta-analysis, money market fund, Murray Gell-Mann, Netflix Prize, pattern recognition, Performance of Mutual Funds in the Period, Philip Mirowski, placebo effect, Ponzi scheme, power law, prediction markets, presumed consent, Richard Thaler, Robert Shiller, statistical model, Steven Pinker, systems thinking, the long tail, The Wisdom of Crowds, ultimatum game, vertical integration

Schelling, Robert J. Shiller, Vernon L. Smith, Erik Snowberg, Cass R. Sunstein, Paul C. Tetlock, Philip E. Tetlock, Hal R. Varian, Justin Wolfers, and Eric Zitzewitz, “The Promise of Prediction Markets,” Science 320 (May 16, 2008):877–878; Bo Cowgill, Justin Wolfers, and Eric Zitzewitz, “Using Prediction Markets to Track Information Flows: Evidence from Google,” working paper, 2008. 4. Phred Dvorak, “Best Buy Taps ‘Prediction Market,’” Wall Street Journal, September 16, 2008. 5. Hilke Plassmann, John O’Doherty, Baba Shiv, and Antonio Rangel, “Marketing Actions Can Modulate Neural Representations of Experienced Pleasantness,” Proceedings of the National Academy of Sciences 105, no. 3 (2008): 1050–1054. 6.

When the dust settled in early 2006, he revealed that the official August forecast of the internal experts was 93 percent accurate, while the presumed amateur crowd was off by only one-tenth of 1 percent.2 Best Buy subsequently allocated additional resources to its prediction market, called TagTrade.3 The market has yielded useful insights for managers through the more than two thousand employees who have made tens of thousands of trades on topics ranging from customer satisfaction scores to store openings to movie sales. For instance, in early 2008, TagTrade indicated that sales of a new service package for laptops would be disappointing when compared with the formal forecast. When early results confirmed the prediction, the company pulled the offering and relaunched it in the fall. While far from flawless, the prediction market has been more accurate than the experts a majority of the time and has provided management with information it would not have had otherwise.4 Sommeliers, Don’t Sniff at This Equation When it comes to wine, I am an ignoramus.

James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations (New York: Doubleday and Company, 2004). 2. Gary Hamel with Bill Breen, The Future of Management (Boston: Harvard Business School Press, 2007), 229–239; Renée Dye, “The Promise of Prediction Markets: A Roundtable,” The McKinsey Quarterly, no. 2 (April 2008): 83–93; and Steve Lohr, “Betting to Improve the Odds,” New York Times, April 9, 2008. 3. Prediction markets are real-money exchanges where people can bet on events with binary and temporally defined outcomes; hence the price reflects the probability of the event occurring. See Kenneth J. Arrow, Robert Forsythe, Michael Gorham, Robert Hahn, Robin Hansen, John O.


pages: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

I do this in Figure 8.3 for both FiveThirtyEight and prediction markets Intrade and PredictIt.8 The brave predictions are the ones that lie at the extreme left and right of the figures. These correspond to a prediction that the Democrat candidate will win or lose a state with a certainty greater than 95 per cent. All of these brave predictions, for both FiveThirtyEight and the prediction markets, proved to be correct: the strong favourite won the state. Figure 8.3 Comparison of predictions and outcome for (a) FiveThirtyEight and (b) prediction markets Intrade/PredictIt for all US states for presidential elections in 2008, 2012 and 2016.

However, the whole point of a prediction market is that it brings together different pieces of information, weighing them in proportion to their quality. So while it would be considered as high quality, it is unlikely that FiveThirtyEight was the sole source of the PredictIt market. And there is certainly no evidence of PredictIt predictions following the ups and downs of FiveThirtyEight with a delay. FiveThirtyEight doesn’t explicitly use betting market data in its model. However, Silver, a former professional gambler, understands very well that prediction markets and bookmakers’ odds give a better reflection of the probability an event will happen than the polls themselves.

Random House. 7 This figure is taken from a project at The Data Face based on data collected by Good Judgement Open: www.thedataface.com/good-judgment-open-election-2016. 8 PredictIt has only been around for one presidential election, but one of its predecessors that works on the same principles, Intrade, was used for both the 2008 and 2012 votes. Alex and I took these prediction markets’ final probability for every state over the three elections, and compared them with FiveThirtyEight’s predictions. 9 Rothschild, D. 2009. ‘Forecasting elections: Comparing prediction markets, polls, and their biases.’ Public Opinion Quarterly 73, no. 5: 895–916. 10 The Brier score is a single number that accounts both for accuracy and the courage of predictions. This score is calculated from the square of the distance between the prediction and the actual outcome.


pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

There are active debates about whether prediction markets provide more accurate forecasts than other methods (such as properly weighted averages of polls, or reliance on the superforecasters identified by Philip Tetlock and discussed in Chapter 2), but few people anymore doubt that prediction markets can be very effective under the right conditions. As economist Robin Hanson, the scholar who has done the most to advance both the theory and practice of prediction markets, puts it, “Prediction markets reflect a fundamental principle underlying the value of market-based pricing: Because information is often widely dispersed among economic actors, it is highly desirable to find a mechanism to collect and aggregate that information. Free markets usually manage this process well because almost anyone can participate, and the potential for profit (and loss) creates strong incentives to search for better information.”

As groups went online and became the crowd, innovators found different ways to detect and harvest this knowledge. Prediction markets were one of the earliest of these, and the ones that built most directly from Hayek’s insights. These are markets not for goods and services, but for future events, such as a particular person being elected US president in 2020, an upcoming movie making between $50 million and $100 million in the box office in its first week, or the official US inflation rate averaging more than 3% over the next quarter. Here’s how prediction markets work. First, the market maker creates a set of securities that participants can buy and sell, just like they sell a company’s shares on the New York Stock Exchange or Nasdaq.

If inflation did, in fact, average more than 3%, all the people holding the “above 3%” security would get $1 for every share they had. Results from prediction markets confirm Hayek’s insights about the knowledge-aggregating power of prices within markets. In markets like the ones just described, events with final share prices of about $0.70 tend to actually happen about 70% of the time, making these prices pretty accurate probability estimates. There are active debates about whether prediction markets provide more accurate forecasts than other methods (such as properly weighted averages of polls, or reliance on the superforecasters identified by Philip Tetlock and discussed in Chapter 2), but few people anymore doubt that prediction markets can be very effective under the right conditions.


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Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley, Jon Kleinberg

Albert Einstein, AltaVista, AOL-Time Warner, Apollo 13, classic study, clean water, conceptual framework, Daniel Kahneman / Amos Tversky, Douglas Hofstadter, Dutch auction, Erdős number, experimental subject, first-price auction, fudge factor, Garrett Hardin, George Akerlof, Gerard Salton, Gerard Salton, Gödel, Escher, Bach, incomplete markets, information asymmetry, information retrieval, John Nash: game theory, Kenneth Arrow, longitudinal study, market clearing, market microstructure, moral hazard, Nash equilibrium, Network effects, Pareto efficiency, Paul Erdős, planetary scale, power law, prediction markets, price anchoring, price mechanism, prisoner's dilemma, random walk, recommendation engine, Richard Thaler, Ronald Coase, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, seminal paper, Simon Singh, slashdot, social contagion, social web, Steve Jobs, Steve Jurvetson, stochastic process, Ted Nelson, the long tail, The Market for Lemons, the strength of weak ties, The Wisdom of Crowds, trade route, Tragedy of the Commons, transaction costs, two and twenty, ultimatum game, Vannevar Bush, Vickrey auction, Vilfredo Pareto, Yogi Berra, zero-sum game

Here, we will ignore the various institutional structures of prediction markets and instead see how much we can discover about them by applying our analysis of horse races via state prices. Consider, for example, the prediction market for the 2008 U.S. Presidential election with two possible outcomes: a Democrat wins or a Republican wins. (The same analysis can handle prediction markets with many plausible outcomes, such as the earlier prediction market for the identity of the Democratic and Republican nominees for President in 2008.) 22.4. PREDICTION MARKETS AND STOCK MARKETS 715 Let fn be the share of the total wealth bet in the prediction market that is bet by trader n.

The state prices are weighted averages of these beliefs, so they too converge to a and b. 22.4 Prediction Markets and Stock Markets Thus far we have been telling a story about horse races, but there is a direct analogy to any market where participants purchase assets whose future value depends on the outcome of uncertain events. Two specific examples are prediction markets and — by far the most consequential application of these ideas — stock markets. In both cases, we will see that state prices play a key role in how we reason about what takes place in the market. Prediction Markets. In a prediction market, individuals trade claims to a one-dollar return conditional on the occurrence of some event.

The final chapter discusses the role of property rights in influencing what outcomes are possible. 22.1 Markets with Exogenous Events In this section we begin examining how markets aggregate opinions about events in settings where the underlying events are exogenous — the probabilities of the events are not affected by the outcomes in the market. Prediction markets are one basic example of this setting. These are markets for (generally very simple) assets which have been created to aggregate individuals’ predictions about a future event into a single group, or market, opinion. In a prediction market, individuals bet on the outcome of some event by trading claims to monetary amounts that are conditional on the outcome of the event. One of the most well-known uses of prediction markets has been for the forecasting of 22.1. MARKETS WITH EXOGENOUS EVENTS 703 election results.


pages: 267 words: 72,552

Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge

accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Apollo 11, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, Bear Stearns, behavioural economics, bitcoin, blockchain, book value, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, cognitive load, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, data science, Didi Chuxing, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, flying shuttle, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, Higgs boson, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, Large Hadron Collider, lone genius, low cost airline, low interest rates, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, radical decentralization, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Robinhood: mobile stock trading app, Sam Altman, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, subprime mortgage crisis, Suez canal 1869, tacit knowledge, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, vertical integration, William Langewiesche, Y Combinator

We purchase “winks” to indicate interest in another person on online dating sites. Firms buy and sell pollution certificates to manage fossil-fuel use. And we set up so-called prediction markets to pool (through money and price) available information on everything from Hollywood box-office receipts to the outcomes of presidential elections. In every one of these marketplaces, price is the key enabler. Consider prediction markets. When participants share their forecasts of a future event, they essentially pool all the information they have. But how do we know what information is accurate and relevant, and what isn’t?

This does not guarantee that all of the predictions in the market will be correct—far from it—but it is eminently better than giving equal weight to every bit of information. Google’s experiments with prediction markets are but one real-world example of the power of combining markets with money to generate more accurate forecasts of future events. Since 2005, employees at the company have been asked to answer questions about potential developments in the tech industry and the world in general. For instance, they may be asked, “How many users will Gmail have at the end of the quarter?” or “Will a Russia office open?” and are offered a range of defined responses. Employees participating in the prediction market are given a wallet of “Goobles” to spend on their answers.

a better-than-even chance to know the truth: Cass R. Sunstein, Infotopia (New York: Oxford University Press, 2006), 25ff. the probability of events related to Google projects: Other companies have also experimented with prediction markets, but Google’s appear to be the largest and longest experiments conducted in the corporate world. See Bo Cowgill, Justin Wolfers, and Eric Zitzewitz, “Using Prediction Markets to Track Information Flows: Evidence from Google,” in Sanmay Das, Michael Ostrovsky, David Pennock, and Boleslaw K. Szymanski, eds., Auctions, Market Mechanisms and Their Applications (Berlin: Springer, 2009), 3, http://link.springer.com/chapter/10.1007/978-3-642-03821-1_2.


pages: 288 words: 81,253

Thinking in Bets by Annie Duke

banking crisis, behavioural economics, Bernie Madoff, Cass Sunstein, cognitive bias, cognitive dissonance, cognitive load, Daniel Kahneman / Amos Tversky, delayed gratification, Demis Hassabis, disinformation, Donald Trump, Dr. Strangelove, en.wikipedia.org, endowment effect, Estimating the Reproducibility of Psychological Science, fake news, Filter Bubble, Herman Kahn, hindsight bias, Jean Tirole, John Nash: game theory, John von Neumann, loss aversion, market design, mutually assured destruction, Nate Silver, p-value, phenotype, prediction markets, Richard Feynman, ride hailing / ride sharing, Stanford marshmallow experiment, Stephen Hawking, Steven Pinker, systematic bias, TED Talk, the scientific method, The Signal and the Noise by Nate Silver, urban planning, Walter Mischel, Yogi Berra, zero-sum game

Justice Thomas’s remark about his hiring practices, including his adaptation of the famous line often attributed to Mark Twain about teaching a pig to sing, has been widely reported, including in David Savage’s profile, “Clarence Thomas Is His Own Man,” in the Los Angeles Times, July 3, 2011. Wanna bet (on science)?: Several studies about corporate prediction markets mention the companies studied or those known to be testing prediction markets. See Cowgill, Wolfers, and Zitzewitz, “Using Prediction Markets to Track Information Flows.” Some studies also refer to some of the companies anonymously. For an example of a study doing both, see Cowgill and Zitzewitz, “Corporate Prediction Markets, Evidence from Google, Ford, and Firm X.” Both citations appear in the Selected Bibliography and Recommendations for Further Reading.

Aired September 30, 1993, on NBC. Cialdini, Robert. Influence: The Psychology of Persuasion. Rev. ed. New York: HarperCollins, 2009. Cowgill, Bo, Justin Wolfers, and Eric Zitzewitz. “Using Prediction Markets to Track Information Flows: Evidence from Google,” January 2009. http://users.nber.org/~jwolfers/papers/GooglePredictionMarketPaper.pdf. Cowgill, Bo, and Eric Zitzewitz. “Corporate Prediction Markets: Evidence from Google, Ford, and Firm X.” Review of Economic Studies 82, no. 4 (April 2, 2015): 1309–41. Dalio, Ray. Principles: Life and Work. New York: Simon & Schuster, 2017. Dawkins, Richard.

Anna Dreber, a behavioral economist at the Stockholm School of Economics, with several colleagues set up a betting market based on these replication attempts. They recruited a bunch of experts in the relevant fields and asked their opinions on the likelihood the Reproducibility Project would replicate the results of forty-four studies. They then gave those experts money to bet on each study’s replication in a prediction market. Experts engaging in traditional peer review, providing their opinion on whether an experimental result would replicate, were right 58% of the time. A betting market in which the traders were the exact same experts and those experts had money on the line predicted correctly 71% of the time.


pages: 247 words: 64,986

Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own by Garett Jones

behavioural economics, centre right, classic study, clean water, corporate governance, David Ricardo: comparative advantage, en.wikipedia.org, experimental economics, Flynn Effect, Gordon Gekko, greed is good, hive mind, invisible hand, Kenneth Arrow, law of one price, meta-analysis, prediction markets, Robert Gordon, Ronald Coase, Saturday Night Live, social intelligence, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tyler Cowen, wikimedia commons, zero-sum game

Bo Cowgill of Google worked with economists Justin Wolfers and Eric Zitzewitz to study Google’s internal prediction markets, an online market in which Google employees can bet on questions such as “How many users will Gmail have” a few months in the future, what Google Talk’s quality rating will be, and whether Google will open an office in Russia.23 These markets work like sports betting markets, so players have a real incentive to guess correctly: the closer their guess is to the truth, the more they can win. Googlers don’t bet for real money, but they can win prizes if they do well in the prediction markets, so the incentives to try hard are reasonably strong.

Googlers don’t bet for real money, but they can win prizes if they do well in the prediction markets, so the incentives to try hard are reasonably strong. As my colleague Robin Hanson notes, these internal prediction markets do a good job guessing real corporate outcomes—and are even better than the guesses of company managers: when money and prizes are on the line, passions and egos lose out, and the truth tends to bubble up. So what shapes the way people bet in these prediction markets? Cowgill’s study found that the best predictor of how any individual Googler bet in the market was whom he or she sat closest to: [O]pinions on specific topics are correlated among employees who are proximate in some sense. Physical proximity was the most important of the forms of proximity we studied . . .

Farrar, Green, Green, Nickerson, and Shewfelt, “Does Discussion Group Composition Affect Policy Preferences?” 22. Farrar and others, “Does Discussion Group Composition Affect Policy Preferences?” p. 637. 23. Cowgill, Wolfers, and Zitzewitz, “Using Prediction Markets to Track Information Flows.” In particular, see Table 1. 24. Cowgill, Wolfers, and Zitzewitz, “Using Prediction Markets to Track Information Flows,” 3. 25. Thomas Jefferson, “Letter to Charles Yancey. 26. Hochschild, “If Democracies Need Informed Voters,” 119. 27. Hochschild, “If Democracies Need Informed Voters,” 120. 28. Brennan, “The Right to a Competent Electorate.”


pages: 80 words: 21,077

Stake Hodler Capitalism: Blockchain and DeFi by Amr Hazem Wahba Metwaly

altcoin, Amazon Web Services, bitcoin, blockchain, business process, congestion charging, COVID-19, crowdsourcing, cryptocurrency, Ethereum, ethereum blockchain, fiat currency, information security, Internet of things, Network effects, non-fungible token, passive income, prediction markets, price stability, Satoshi Nakamoto, seigniorage, Skype, smart contracts, underbanked, Vitalik Buterin

DeFi payment solutions build a more free economic system for the underbanked and unbanked populace and help big financial institutions streamlining market infrastructure and serve better wholesale and retail customers. Prediction markets Blockchain-based prediction markets impede the crowd's reasoning and help users vote and exchange value on the result of events. Market prices then become crowdsourced pointers of the odds of an event. An example of a popular DeFi betting platform is Augur; it accentuates prediction markets based on election results, economic events, sports games, to mention but a few. Saving Many DeFi apps offer interest-bearing accounts, which can be exponentially more than the conventional savings accounts type based on a dynamic interest rate linked to supply and demand when sealed into lending pool policies like Compound.

Government Management Automobile Real estate Healthcare Benefits of Smart Contracts Problems of Smart Contracts Chapter 4: Ethereum and DeFi Popular DeFi Apps: Lending and borrowing MakerDAO DAI Overview Stable-coin With Cryptocurrency Decentralized Exchange Degrees of Decentralization Compound Derivatives DAOs Prediction markets Saving Staking Tokenization Trading DeFi vs. CeFi Differences between DeFi and Open Banking Why the Hype? Benefits of DeFi Advantages of DeFi DeFi and Potential Risks Chapter 5: What Does Yield Farming In Decentralized Finance (DeFi) Mean? Don't You Need A Lot of Money To Run A Bank?


pages: 230 words: 61,702

The Internet of Us: Knowing More and Understanding Less in the Age of Big Data by Michael P. Lynch

Affordable Care Act / Obamacare, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bitcoin, Cass Sunstein, Claude Shannon: information theory, cognitive load, crowdsourcing, data science, Edward Snowden, Firefox, Google Glasses, hive mind, income inequality, Internet of things, John von Neumann, meta-analysis, Nate Silver, new economy, Nick Bostrom, Panopticon Jeremy Bentham, patient HM, prediction markets, RFID, sharing economy, Steve Jobs, Steven Levy, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Twitter Arab Spring, WikiLeaks

In a certain obvious sense, markets like this can be seen as encoding the information of the network of investors that make it up—not only about what may happen in the future but about the state of an election at any given time. But the way in which this information is aggregated is not, as in the cases above, statistical. Prediction markets don’t average the views of their participants, rather they work in the same way other markets work: the more confident buyers are that a given candidate will win, the higher his or her “stock” goes—the more value is attached to it, no matter how many people may own that stock. But prediction markets also have their limits. A well-known example, noted by David Leonhardt of the New York Times, was the 2012 Supreme Court decision on the Affordable Care Act.9 Right up to the last minute, Intrade was indicating a 75 percent chance that the Act’s mandate would be declared unconstitutional.

In that case, the crowd is not wise but unwise. This brings us to a key point: whether or not a network “knows” something (even in the nonliteral sense) depends on the cognitive capacities (and incapacities) of the nodes on that network—the individual people who make it up. A good example is prediction markets. Markets like this trade in futures, but participants aren’t betting on whether a given company’s monetary value will rise but whether, for example, a politician will win an election or a particular movie will win an Oscar. These markets had some notable early successes; Intrade, for example, was famously better at predicting the 2006 midterm elections than cable news.

If so, then perhaps the best we can say is that what we can loosely call the group’s implicit commitment “supervenes” or is a product of the individuals’ commitments. 8. Surowiecki, The Wisdom of Crowds, xii. 9. David Leonhardt, “When the Crowd Isn’t Wise,” New York Times, July 7, 2012. 10. Nate Silver, “The Virtues and Vices of Election Prediction Markets,” New York Times, October 24, 2012. 11. I was helped to see these points in discussions with Sandy Goldberg and Nate Sheff. The example in the text is similar to that in Goldberg, “The Division of Epistemic Labor,” 117. 12. Weinberger, Too Big to Know, 21. 13. Descartes, Meditations, 103. 14.


pages: 733 words: 179,391

Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

Alan Greenspan, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, bitcoin, Bob Litterman, Bonfire of the Vanities, bonus culture, break the buck, Brexit referendum, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, carbon tax, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, confounding variable, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, democratizing finance, Diane Coyle, diversification, diversified portfolio, do well by doing good, double helix, easy for humans, difficult for computers, equity risk premium, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, information security, interest rate derivative, invention of the telegraph, Isaac Newton, it's over 9,000, James Watt: steam engine, Jeff Hawkins, Jim Simons, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, language acquisition, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, megaproject, merger arbitrage, meta-analysis, Milgram experiment, mirror neurons, money market fund, moral hazard, Myron Scholes, Neil Armstrong, Nick Leeson, old-boy network, One Laptop per Child (OLPC), out of africa, p-value, PalmPilot, paper trading, passive investing, Paul Lévy, Paul Samuelson, Paul Volcker talking about ATMs, Phillips curve, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Solow, Sam Peltzman, Savings and loan crisis, seminal paper, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, subprime mortgage crisis, survivorship bias, systematic bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, uptick rule, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

The Wall Street odds represent a large body of extremely impartial opinion that talks with money and approaches Coolidge and Davis as dispassionately as it pronounces judgment on Anaconda and Bethlehem Steel.”45 With the advent of the Internet, prediction markets have experienced something of a renaissance. The Iowa Electronic Markets—originally the Iowa Presidential Stock Market—is perhaps the best-known research prediction market, dating back to the 1988 presidential election,46 but commercial prediction markets have also become popular. Many of these markets have done as well or better than conventional forecasting methods; for instance, in midsummer of 2010 the Iowa Electronic Markets correctly predicted that the Republican Party would take control of the House of Representatives, but that the Democratic Party would keep control of the Senate, a result few people expected.

On the other hand, in some particularly rancorous political races, such as the 2012 presidential election, people have tried to manipulate prediction markets in attempts to gain “momentum” for their preferred candidate, in much the same way an unscrupulous trader might try to run up the price of a penny stock. While this had no practical way of affecting the outcome of the election (at least, not given the small size of these markets), it did briefly damage the usefulness of the prediction market before it corrected itself. In the end, however, people with the more accurate prediction gained more money at the manipulators’ expense, just as the Efficient Markets Hypothesis would predict. But prediction markets are only one possible use of the Efficient Markets Hypothesis.

If the Efficient Markets Hypothesis holds, the market price fully reflects all available information in the crowd. What a fantastic way to gather information. And not just about the Red Sox; imagine creating prediction markets for collecting information about terrorist events, flu epidemics, nuclear meltdowns, and presidential elections. This may sound a little like science fiction, but prediction markets were widely used in the United States in the nineteenth and early twentieth centuries to forecast elections before the advent of modern polling techniques.44 Paul W. Rhode and Koleman Strumpf have documented these early prototypes—they were betting markets with standardized contracts.


pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske, Jack Tatar

Airbnb, Alan Greenspan, altcoin, Alvin Toffler, asset allocation, asset-backed security, autonomous vehicles, Bear Stearns, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, book value, business cycle, business process, buy and hold, capital controls, carbon tax, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, fixed income, Future Shock, general purpose technology, George Gilder, Google Hangouts, high net worth, hype cycle, information security, initial coin offering, it's over 9,000, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, low interest rates, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, quantum cryptography, RAND corporation, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seminal paper, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, two and twenty, Uber for X, Vanguard fund, Vitalik Buterin, WikiLeaks, Y2K

DECENTRALIZED PLATFORMS TO PREDICT THE FUTURE One of the more interesting dApps in development uses Ethereum’s blockchain to facilitate prediction markets. The company Augur seeks to provide a platform that allows users to wager on the results of any event, creating a market for people to test their predictions.34 Hence the term “prediction market.” For instance, if someone had sought to predict whether Donald Trump or Hillary Clinton would win the 2016 U.S. presidential election, he or she could have used Augur to create a prediction market and wager against others on the outcome (if the service had been up and running at the time). Augur uses a cryptotoken, which it calls Reputation (REP), to incentivize people to report on the outcomes of events truthfully.

By that time, he had already garnered the support of over 15 developers and dozens in the community outreach team.9 In Ethereum’s white paper that initially described its inner workings, Buterin’s team made no qualms about their aspirations: What is more interesting about Ethereum, however, is that the Ethereum protocol moves far beyond just currency. Protocols around decentralized file storage, decentralized computation and decentralized prediction markets, among dozens of other such concepts, have the potential to substantially increase the efficiency of the computational industry, and provide a massive boost to other peer-to-peer protocols by adding for the first time an economic layer.10 Importantly, Buterin did not intend for Ethereum and its native asset, ether, to be a minor variation on Bitcoin’s codebase.

For instance, if someone had sought to predict whether Donald Trump or Hillary Clinton would win the 2016 U.S. presidential election, he or she could have used Augur to create a prediction market and wager against others on the outcome (if the service had been up and running at the time). Augur uses a cryptotoken, which it calls Reputation (REP), to incentivize people to report on the outcomes of events truthfully. These reporters are different from the people wagering on the outcome of events. The problem with a decentralized prediction market is that there’s no centralized authority on the outcome of events. Augur uses REP to reward people who report truthfully and penalize those who lie. Augur explains it as follows: Those who hold Reputation are expected to report accurately on the outcome of randomly selected events within Augur every few weeks. If holders fail to report accurately on the outcome of an event, or attempt to be dishonest—the Augur system redistributes the bad reporter’s Reputation to those who have reported accurately during the same reporting cycle.35 Augur conducted its own crowdfunding effort in 2015, selling 80 percent of a fixed supply of 11 million REP.


pages: 313 words: 91,098

The Knowledge Illusion by Steven Sloman

Affordable Care Act / Obamacare, Air France Flight 447, attribution theory, bitcoin, Black Swan, Cass Sunstein, combinatorial explosion, computer age, Computing Machinery and Intelligence, CRISPR, crowdsourcing, Dmitri Mendeleev, driverless car, Dunning–Kruger effect, Elon Musk, Ethereum, Flynn Effect, Great Leap Forward, Gregor Mendel, Hernando de Soto, Higgs boson, hindsight bias, hive mind, indoor plumbing, Isaac Newton, John von Neumann, libertarian paternalism, Mahatma Gandhi, Mark Zuckerberg, meta-analysis, Nick Bostrom, obamacare, Peoples Temple, prediction markets, randomized controlled trial, Ray Kurzweil, Richard Feynman, Richard Thaler, Rodney Brooks, Rosa Parks, seminal paper, single-payer health, speech recognition, stem cell, Stephen Hawking, Steve Jobs, technological singularity, The Coming Technological Singularity, The Wisdom of Crowds, Vernor Vinge, web application, Whole Earth Review, Y Combinator

A group of economists has sung the praises of a type of crowdsourcing called a prediction market. In a prediction market, people make bets about what will happen in the future. The amount that the crowd is willing to bet on a particular outcome is used to estimate the probability of that outcome. People are motivated to bet because the best predictor wins a prize like money or prestige. Experts are especially motivated because they are more likely to know what will happen than novices, and so experts tend to have an outsize influence on the market. Many government agencies and private companies have used prediction markets to make guesses about what will happen in domestic elections, international affairs, and business environments.

within 1 percent of the ox’s true weight of 1,198 pounds: Despite frequent reports saying otherwise, he did not find that the mean weight was within 1 pound of the ox’s true weight. Nor did he find that the average was better than any individual guess. prediction market: K. J. Arrow, R. Forsythe, M. Gorham, R. Hahn, R. Hanson, J. O. Ledyard, S. Levmore, et al. (2008). “The Promise of Prediction Markets.” Science 320(5878): 877–878. EIGHT. THINKING ABOUT SCIENCE a giant sledgehammer: smithsonianmag.com/history/what-the-luddites-really-fought-against-264412/?all. increasingly bizarre: Ibid. James Inhofe’s snowball: washingtonpost.com/news/the-fix/wp/2015/02/26/jim-inhofes-snowball-has-disproven-climate-change-once-and-for-all.

See chaos theory consequences vs. values arguments, 182–87 contribution of individuals example of group thinking, 122 Copernicus, Nicolaus, 198–99 counterfactual thought, 64–65 Galileo’s experiments with dropping different weights, 65–66 imagining scenarios to figure out likely outcomes, 66 crowdsourcing expertise, 146–50 ox’s weight example, 148 Pallokerho-35 Finnish soccer club example, 148 prediction market, 149 user ratings, 148 crows ability to reason diagnostically, 62 CRT (Cognitive Reflection Test), 80–84 bat and ball problem, 81 lily pad problem, 81–82 machines and widgets problem, 82 crystallized intelligence, 202 cult communities, 260 cultural values and cognition, 160–63 reconciling conflicting beliefs, 161–62 “Science Mike” (Mike McHargue), 160–62 cumulative culture, 117–18 curse of knowledge, 128, 244 curving bullets example of physics, 69–70 Dalio, Ray, 253 Damasio, Antonio, 103 decentralized collaborative activity, 149–50 Bitcoin, 150 block chain technology, 150 Ethereum, 150 decision-making, 103–05, 240, 241, 248–49, 250–53 deficit model of science attitudes, 157–60 Dehghani, Morteza, 185–86 Descartes, René, 87 de Soto, Hernando, 244–45 DeVito, Danny, 45–46 Dewey, John, 216 diagnostic reasoning, 58–62 crow example, 62 lethargy example, 59–61 diSessa, Andrea, 71 disgust, feelings of, 104–05 division of cognitive labor, 14, 109–11, 120–21, 128–29 area of expertise example, 120 car analogy example, 207–08 in the field of science, 222–23 household finances, 247 wine expert example, 120 dogs Cassie example, 49–50 Pavlovian conditioning, 50–51 doorway example of optic flow, 99–100 driving ability example of ignorance, 257–58 Dunbar, Robin, 113 Dunning, David, 257–58 Dunning-Kruger effect, 258 Eastwood, Clint, 172 economics of science, 227–28 education application of classroom learning, 216–17 becoming a car mechanic example, 219–20 expressing desire to learn that which is unknown, 221 financial issues, 240–41 history of Spain example, 220 Ignorance course, 221 illusion of comprehension, 217–18 just-in-time, 251–52 learning to accept what you don’t know, 220–21 mathematical abilities of Brazilian children, 215–16 peer, 230–31 purpose of, 219–21 teaching science, 222, 225–32 Einstein, Albert, 199 embodied intelligence, 91–93 embodiment, 102 emotional responses that influence decision-making, 103–05, 240 engagement as a human concept, 117 environment, knowledge of your personal, 94–96 Ethereum, 150 expertise and crowdsourcing, 146–50 in scientific matters, 226–27 to understand community issues, 188–89 explanation foes and fiends, 237–39 advertising, 239–40, 241–42 Band-Aids example, 237–38 skin care example, 239–40 vesting service letter example, 243–44 explorers’ self-confidence, 263 eyesight.


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

Alan Greenspan, Albert Einstein, Bear Stearns, Bill Joy: nanobots, Black-Scholes formula, business cycle, call centre, carbon tax, cloud computing, collateralized debt obligation, commoditize, Computing Machinery and Intelligence, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, Mitch Kapor, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Robert Solow, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, technological singularity, the long tail, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty, warehouse automation, warehouse robotics

The Predictive Nature of Markets One of the more interesting developments to arise out of the Internet has been the appearance of online prediction markets. A prediction market is really just another name for a betting market, and it operates in a similar fashion to the futures markets that allow traders to place bets on the future direction of things like oil prices and stock market indexes. Prediction markets, such as the Iowa Electronic Markets (IEM) and Intrade, allow participants to bet real money on things like elections, economic developments (such as recessions), or specific events in the business or entertainment worlds. While prediction markets are specifically set up to predict future events, we know that we can expand this idea and say that all free markets are, in essence, prediction markets.

While prediction markets are specifically set up to predict future events, we know that we can expand this idea and say that all free markets are, in essence, prediction markets. If you buy a particular company’s stock, then you are placing a bet that, in the future, that stock will trade at a higher value. Collectively, the millions of participants in the world’s stock markets often act as a sort of predictive barometer for the economy as a whole. Historically, the U.S. stock market has often anticipated recessions by six months or so. Likewise, recovery from a recession is very often preceded by a rise in the stock market. This predictive feature also applies to all the other various markets with which we interact, including the housing market, the job market, and the mass market for goods and services.


One Up on Wall Street by Peter Lynch

air freight, Apple's 1984 Super Bowl advert, Boeing 747, book value, buy and hold, Carl Icahn, corporate raider, cuban missile crisis, Donald Trump, fixed income, index fund, Irwin Jacobs, Isaac Newton, junk bonds, large denomination, money market fund, prediction markets, random walk, shareholder value, Silicon Valley, Teledyne, vertical integration, Y2K, Yom Kippur War, zero-sum game

This period of my childhood, and not the recent 1980s, was truly the greatest bull market in history, but to hear it from my uncles, you’d have thought it was the craps game behind the pool hall. “Never get involved in the market,” people warned. “It’s too risky. You’ll lose all your money.” Looking back on it, I realize there was less risk of losing all one’s money in the stock market of the 1950s than at any time before or since. This taught me not only that it’s difficult to predict markets, but also that small investors tend to be pessimistic and optimistic at precisely the wrong times, so it’s self-defeating to try to invest in good markets and get out of bad ones. My father, an industrious man and former mathematics professor who left academia to become the youngest senior auditor at John Hancock, got sick when I was seven and died of brain cancer when I was ten.

Please Don’t Ask During every question-and-answer period after I give a speech, somebody stands up and asks me if we’re in a good market or a bad market. For every person who wonders if Goodyear Tire is a solid company, or well-priced at current levels, four other people want to know if the bull is alive and kicking, or if the bear has shown its grizzly face. I always tell them the only thing I know about predicting markets is that every time I get promoted, the market goes down. As soon as those words are launched from my lips, somebody else stands up and asks me when I’m due for another promotion. Obviously you don’t have to be able to predict the stock market to make money in stocks, or else I wouldn’t have made any money.

Every year I talk to the executives of a thousand companies, and I can’t avoid hearing from the various gold bugs, interest-rate disciples, Federal Reserve watchers, and fiscal mystics quoted in the newspapers. Thousands of experts study overbought indicators, oversold indicators, head-and-shoulder patterns, put-call ratios, the Fed’s policy on money supply, foreign investment, the movement of the constellations through the heavens, and the moss on oak trees, and they can’t predict markets with any useful consistency, any more than the gizzard squeezers could tell the Roman emperors when the Huns would attack. Nobody sent up any warning flares before the 1973–74 stock market debacle, either. Back in graduate school I learned the market goes up 9 percent a year, and since then it’s never gone up 9 percent in a year, and I’ve yet to find a reliable source to inform me how much it will go up, or simply whether it will go up or down.


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

Alan Greenspan, Alvin Roth, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, benefit corporation, Bernie Madoff, buy and hold, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, cognitive dissonance, collateralized debt obligation, collective bargaining, computer age, corporate governance, Daniel Kahneman / Amos Tversky, democratizing finance, Deng Xiaoping, diversification, diversified portfolio, Donald Trump, Edward Glaeser, eurozone crisis, experimental economics, financial engineering, financial innovation, financial thriller, fixed income, full employment, fundamental attribution error, George Akerlof, Great Leap Forward, Ida Tarbell, income inequality, information asymmetry, invisible hand, John Bogle, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, land reform, loss aversion, Louis Bachelier, Mahatma Gandhi, Mark Zuckerberg, market bubble, market design, means of production, microcredit, moral hazard, mortgage debt, Myron Scholes, Nelson Mandela, Occupy movement, passive investing, Ponzi scheme, prediction markets, profit maximization, quantitative easing, random walk, regulatory arbitrage, Richard Thaler, Right to Buy, road to serfdom, Robert Shiller, Ronald Reagan, selection bias, self-driving car, shareholder value, Sharpe ratio, short selling, Simon Kuznets, Skype, social contagion, Steven Pinker, tail risk, telemarketer, Thales and the olive presses, Thales of Miletus, The Market for Lemons, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, Vanguard fund, young professional, zero-sum game, Zipcar

With the advent of high-scope trading, many more things are traded. An example would be the “prediction markets” that set prices based on the probabilities that a specified event will transpire. The earliest prediction markets, dating back to 1988 at the Iowa Electronic Markets, traded contracts that paid out according to the outcome of a speci ed event, such as a candidate receiving a certain share of the popular vote. The price of such a contract turns out to be a useful estimate of the probability of that outcome.11 Since prediction markets focus on a well-de ned event in the near future and give quick feedback to participants, they are probably less likely to be in uenced by speculative excesses than other markets, such as stock markets.

The price of such a contract turns out to be a useful estimate of the probability of that outcome.11 Since prediction markets focus on a well-de ned event in the near future and give quick feedback to participants, they are probably less likely to be in uenced by speculative excesses than other markets, such as stock markets. Today there are many such prediction markets, including Intrade.com, Lumenogic at newsfutures.com, and the Foresight Exchange at ideasphere.com. There have been attempts to start markets for macroeconomic aggregates.12 In 1985 the U.S. Co ee, Sugar and Cocoa Exchange launched a market for the U.S. consumer price index, and in 2002 Goldman Sachs launched a market for nonfarm labor force statistics. Analogous markets have since been trading at the CME Group in Chicago.13 The European Investment Bank in 2004 attempted to start a market for longevity risk— the risk that, because of developments in medical research and changing environmental conditions, people will on average live longer than expected (a problem for de nedbene t pension funds, which have to make payments for as long as retirees live) or less long than expected (a problem for life insurance companies, which will have to pay benefits early).14 Many of these markets have not yet achieved liquidity, and there is little volume of trade.

Analogous markets have since been trading at the CME Group in Chicago.13 The European Investment Bank in 2004 attempted to start a market for longevity risk— the risk that, because of developments in medical research and changing environmental conditions, people will on average live longer than expected (a problem for de nedbene t pension funds, which have to make payments for as long as retirees live) or less long than expected (a problem for life insurance companies, which will have to pay benefits early).14 Many of these markets have not yet achieved liquidity, and there is little volume of trade. Often, as in prediction markets, the volume is so low that the market seems more a game among enthusiasts than a signi cant economic institution. This problem poses a challenge for market makers, and it is instructive to understand the role they play in launching such markets. For example, there was, until recently, no derivatives market for residential real estate prices, for single-family home prices.


pages: 309 words: 54,839

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts by David Gerard

altcoin, Amazon Web Services, augmented reality, Bernie Madoff, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, Californian Ideology, clean water, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, distributed ledger, Dogecoin, Dr. Strangelove, drug harm reduction, Dunning–Kruger effect, Ethereum, ethereum blockchain, Extropian, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, functional programming, index fund, information security, initial coin offering, Internet Archive, Internet of things, Kickstarter, litecoin, M-Pesa, margin call, Neal Stephenson, Network effects, operational security, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, Potemkin village, prediction markets, quantitative easing, RAND corporation, ransomware, Ray Kurzweil, Ross Ulbricht, Ruby on Rails, Satoshi Nakamoto, short selling, Silicon Valley, Silicon Valley ideology, Singularitarianism, slashdot, smart contracts, South Sea Bubble, tulip mania, Turing complete, Turing machine, Vitalik Buterin, WikiLeaks

The ideas themselves are as bad as the worst dot-com IPOs. Digix, the first token crowdsale on the Ethereum blockchain itself, is a cryptocurrency backed by gold;309 Golem offers a “decentralized” (buzzword alert!) market in computing, like Amazon Web Services except you can only pay using their token;310 Gnosis offers semiautomatic prediction markets using their token;311 SingularDTV is a bizarre plan to fund a TV show about the Singularity in which a Caribbean island adopts Ethereum as its currency and Austrian economics works (this one gets its own section later in the book); Iconomi is an index fund of other ICOs.312 The token smart contracts are often incompetent in both intended functionality and programming ability.313 This turns out not to matter as long as they do the basic job: attract buyers and sell tokens.

Or, as Matt Levine from Bloomberg points out: “My immutable unforgeable cryptographically secure blockchain record proving that I have 10,000 pounds of aluminum in a warehouse is not much use to a bank if I then smuggle the aluminum out of the warehouse through the back door.”335 Technology and business journalists writing about non-cryptocurrency use cases for smart contracts never seem to mention that their “trustless” system will still involve trusting humans wherever it touches the physical world. You may have a tamperproof system for running contract code, but the inputs have to come from outside this secure space. A common proposal is to outsource your oracle to a prediction market – humans betting on predictions – that is also on your blockchain, such as Augur. Somehow, the outcome of a bet is supposed to substitute for direct knowledge of an event having happened or not, with sufficient confidence in the process to let it affect your money. If your question isn’t popular enough to attract sufficient uninvolved wagers – it would often be worth it for one party to just bribe the bettors – you will still have the oracle problem in determining whether the event has in fact occurred.

You can’t get rid of the human element by adding another layer of indirection – it’s oracles all the way down. (Augur has openly bragged that they think running on a blockchain means they can dodge US government regulation on gambling and derivatives, which led to the shutdown of previous prediction markets, despite being a single company with known principals.336) Immutability: make your mistakes unfixable The value proposition of “immutability” is that nobody can mess with your contract once it’s been deployed. The common pitch to musicians, for example, is that the big record label will have to pay you as it says in your contract, quickly and automatically.


pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

airport security, Alan Greenspan, Alvin Toffler, An Inconvenient Truth, availability heuristic, Bayesian statistics, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, book value, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Charles Babbage, classic study, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, disinformation, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Ford Model T, Freestyle chess, fudge factor, Future Shock, George Akerlof, global pandemic, Goodhart's law, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, Japanese asset price bubble, John Bogle, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, Oklahoma City bombing, PageRank, pattern recognition, pets.com, Phillips curve, Pierre-Simon Laplace, Plato's cave, power law, prediction markets, Productivity paradox, proprietary trading, public intellectual, random walk, Richard Thaler, Robert Shiller, Robert Solow, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, SimCity, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Timothy McVeigh, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, Wayback Machine, wikimedia commons

The more fundamental problem is that we have a demand for experts in our society but we don’t actually have that much of a demand for accurate forecasts.” Hanson, in order to address this deficiency, is an advocate of prediction markets—systems where you can place bets on a particular economic or policy outcome, like whether Israel will go to war with Iran, or how much global temperatures will rise because of climate change. His argument for these is pretty simple: they ensure that we have a financial stake in being accurate when we make forecasts, rather than just trying to look good to our peers. We will revisit the idea of prediction markets in chapter 11; they are not a panacea, particularly if we make the mistake of assuming that they can never go wrong.

In fact, a market that makes perfect predictions is a logical impossibility. Justin Wolfers, Prediction Markets Cop If there really were a Bayesland, then Justin Wolfers, a fast-talking, ponytailed polymath who is among America’s best young economists, would be its chief of police, writing a ticket anytime he observed someone refusing to bet on their forecasts. Wolfers challenged me to a dinner bet after I wrote on my blog that I thought Rick Santorum would win the Iowa caucus, bucking the prediction market Intrade (as well as my own predictive model), which still showed Mitt Romney ahead. In that case, I was willing to commit to the bet, which turned out well for me after Santorum won by literally just a few dozen votes after a weeks-long recount.* But there have been other times when I have been less willing to accept one of Wolfers’ challenges.

I visited Wolfers at his home, where he was an outstanding host, having ordered a full complement of hoagie sandwiches from Sarcone’s to welcome me, my research assistant Arikia Millikan, and one of his most talented students, David Rothschild. But he was buttering me up for a roast. Wolfers and Rothschild had been studying the behavior of prediction markets like Intrade, a sort of real-life version of Bayesland in which traders buy and sell shares of stock that represent real-world news predictions—everything from who will win the Academy Award for Best Picture to the chance of an Israeli air strike on Iran. Political events are especially popular subjects for betting.


Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

Abraham Maslow, Abraham Wald, affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, Apollo 13, Apple Newton, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, dark pattern, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Dunning–Kruger effect, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fake news, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, if you see hoof prints, think horses—not zebras, Ignaz Semmelweis: hand washing, illegal immigration, imposter syndrome, incognito mode, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, karōshi / gwarosa / guolaosi, lateral thinking, loss aversion, Louis Pasteur, LuLaRoe, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nocebo, nuclear winter, offshore financial centre, p-value, Paradox of Choice, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, power law, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Streisand effect, sunk-cost fallacy, survivorship bias, systems thinking, The future is already here, The last Blockbuster video rental store is in Bend, Oregon, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, vertical integration, Vilfredo Pareto, warehouse robotics, WarGames: Global Thermonuclear War, When a measure becomes a target, wikimedia commons

If more people are making yes predictions than no predications, then the price of the stock rises, and vice versa. By looking at the current prices in the prediction market, you can get a sense of what the market thinks will happen, based on how people are betting (buying shares). Many big companies operate similar prediction markets internally, where employees can predict the outcome of things like sales forecasts and marketing campaigns. Several larger public prediction markets also exist, such as PredictIt, which focuses on political predictions in the manner described above. While this market has successfully predicted many election outcomes across the world, in 2016 it failed to correctly predict both the election of Donald Trump and the UK’s Brexit vote.

In general, drawing on collective intelligence makes sense when the group’s collective pool of knowledge is greater than what you could otherwise get access to; this helps you arrive at a more intelligent decision than you would arrive at on your own. “The crowd” can help systematically think through various scenarios, get new data and ideas, or simply help improve existing ideas. One direct application of crowdsourcing to scenario analysis is the use of a prediction market, which is like a stock market for predictions. In a simple formulation of this concept, the price of each stock can range between $0 and $1 and represents the market’s current probability of an event taking place, such as whether a certain candidate will be elected. For example, a price of $0.59 would represent a 59 percent probability that the candidate would be elected.

While this market has successfully predicted many election outcomes across the world, in 2016 it failed to correctly predict both the election of Donald Trump and the UK’s Brexit vote. Retrospective analysis showed that diversity of opinion seemed lacking and that participants in the prediction market likely didn’t have enough direct contact with Trump or Brexit supporters. In addition, predictors were not operating fully independently, instead being influenced by the initial outsized odds against Trump and Brexit. Another project, called the Good Judgment Project, crowdsources predictions for world events. Its co-creator, Philip E.


pages: 179 words: 42,081

DeFi and the Future of Finance by Campbell R. Harvey, Ashwin Ramachandran, Joey Santoro, Vitalik Buterin, Fred Ehrsam

activist fund / activist shareholder / activist investor, bank run, barriers to entry, bitcoin, blockchain, collateralized debt obligation, crowdsourcing, cryptocurrency, David Graeber, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, Future Shock, initial coin offering, Jane Street, margin call, money: store of value / unit of account / medium of exchange, Network effects, non-fungible token, passive income, peer-to-peer, prediction markets, rent-seeking, RFID, risk tolerance, Robinhood: mobile stock trading app, Satoshi Nakamoto, seigniorage, smart contracts, transaction costs, Vitalik Buterin, yield curve, zero-coupon bond

This book provides a peek into that future, and you, the reader, hold the power to create it. Fred Ehrsam Co-founder and Managing Partner, Paradigm Co-founder, Coinbase PREFACE Decentralized finance (or DeFi) has always been a big part of what I hoped to see people build on Ethereum. Ideas around user-issued assets, stablecoins, prediction markets, decentralized exchanges, and much more had already been at the top of my mind as well as the minds of many others trying to build the next stage of blockchain technology in those special early days of 2013–14. But instead of creating a limited platform targeting a set of known existing use cases, as many others did, Ethereum introduced general-purpose programmability, allowing blockchain-based contracts that can hold digital assets and transfer them according to predefined rules, and even support applications with components that are not financial at all.

But instead of creating a limited platform targeting a set of known existing use cases, as many others did, Ethereum introduced general-purpose programmability, allowing blockchain-based contracts that can hold digital assets and transfer them according to predefined rules, and even support applications with components that are not financial at all. People in the Ethereum community started working on applications such as on-chain stablecoins, prediction markets, and exchanges almost immediately, but only after more than five years did the ecosystem truly start to mature. I believe that DeFi will create a new, easy-to-use and globally accessible financial system for the world. For example, applications like stablecoins are some of the most valuable innovations to come out of DeFi so far.

Without oracles, blockchains are completely self-encapsulated and have no knowledge of the outside world other than the transactions added to the native blockchain. Many DeFi protocols require access to secure, tamper-resistant asset prices to ensure that routine actions such as liquidations and prediction market resolutions function correctly. Protocol reliance on these data feeds introduces oracle risk. Oracles represent significant risks to the systems they help support. If an oracle's cost of corruption is ever less than an attacker's potential profit from corruption, the oracle is extremely vulnerable to attack.


Trend Commandments: Trading for Exceptional Returns by Michael W. Covel

Alan Greenspan, Albert Einstein, Alvin Toffler, behavioural economics, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, disinformation, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, global macro, Jim Simons, Lao Tzu, Long Term Capital Management, managed futures, market bubble, market microstructure, Market Wizards by Jack D. Schwager, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Sharpe ratio, systematic trading, the scientific method, three-martini lunch, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

I thought a different approach to get this If you are not story out was required, for very few are yet criticized, you might aware of what’s in these pages. not be doing much. Sadly, many still see making money wrong. They make wildly inaccurate assumptions about what constitutes a winning trader: • Do they possess a unique talent? • A special inborn gene or divine gift? • The innate talent of a child prodigy? • Inside knowledge? • Ability to predict markets? • Degrees in finance or an MBA? • Huge starting capital? One answer: No. Why do we not know that? Instant gratification is our Achilles’ heel. Multitask this and that. Kardashians. Now. Faster. Easier. Patience is a fourletter word. How does the latest iPad help you to make money trading the markets?

It is based on the belief that at any given point in time, market prices reflect all known fundamentals for that particular market. Instead of trying to evaluate fundamental factors, technical analysis looks at market prices themselves. The illusion has been There are essentially two forms of technical analysis. The first is based on reading charts and using indicators to supposedly predict market direction. For example, here is a mixing of fundamentals and predictive technical analysis: created that there is an explanation for everything with the primary task to find that explanation.6 Grains all made subtle bull breakout technical moves last week as continued fear of a global grain panic builds premium into these markets.

Trend followers wait for a market to move first and then they follow it.1 Now, some argue that the term trend following is too imprecise. Others use terms like global statistical financial analysis or managed futures to describe the strategy. That debate will not be solved here. If you do not like the phrase trend following, substitute your term as you keep reading. Trend following trading is reactive. It does not predict market direction. Trend trading demands self-discipline to follow precise rules (no guessing or wild emotions). It involves a certain risk management that uses the current market price, equity level in your account, and current market volatility. We decided that systematic trading was best. Fundamental trading gave me ulcers.2 Trend traders use an initial risk rule to determine their trading size at entry.


pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

algorithmic trading, AltaVista, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, Bitcoin Ponzi scheme, business cycle, business intelligence, business process, business process outsourcing, buy and hold, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, cross-border payments, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, gamification, Google Glasses, high net worth, informal economy, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, MITM: man-in-the-middle, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Salesforce, Satoshi Nakamoto, Silicon Valley, smart cities, social intelligence, software as a service, Steve Jobs, strong AI, Stuxnet, the long tail, trade route, unbanked and underbanked, underbanked, upwardly mobile, vertical integration, We are the 99%, web application, WikiLeaks, Y2K

In the same way, banks can use transaction data combined with search trends and other data to predict and then proactively offer service in real time. That service might be offering car loans as you drive by the showroom of the BMW dealership you happened to be Googling last night or mortgages as you drive towards the real estate office of the broker you found. Now that’s all well and good, but it goes further than this as prediction marketing can now be embedded into the internet of things. For example, a few years ago the Metro store in Germany built a prototype of the grocery outlet of a few years ahead using NFC and RFID technologies. The concept store included the idea of dynamic pricing as you walk through the aisles, based upon your loyalty, shopping habits and more.

Like algorithmic trading in capital markets where algorithmic news feeds allow trading in equities to move in real-time high frequency blackbox strategies that maximise returns, we’re talking of applying the same technologies to retail transaction services for customer loyalty and wallet share. That’s the battle about to begin as we move from managing data to using information as a competitive weapon and, at the core of predictive marketing is Big Data. If the battleground is augmented service, then Big Data is the weaponry to compete. What is Big Data? The term Big Data stems from the Second World War, when the phrase Big Science was used to describe the rapid cycle of changes that occurred in scientific disciplines during and after World War II.

If you search for pricing TVs, they might offer you a special deal with a retailer’s discount code. If you don’t think that Google Analytics are the key to predictive, proactive marketing, just checkout the results of research of three academics who find that Google predicts stock market movements pretty accurately: “Debt” was the most reliable term for predicting market ups and downs, the researchers found. By going long when “debt” searches dropped and shorting the market when “debt” searches rose, the researchers were able to increase their hypothetical portfolio by 326 percent. (In comparison, a constant buy-and-hold strategy yielded just a 16 percent return.)[24] In the same way, banks can use transaction data combined with search trends and other data to predict and then proactively offer service in real time.


Future Files: A Brief History of the Next 50 Years by Richard Watson

Abraham Maslow, Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon credits, carbon footprint, carbon tax, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, digital nomad, disintermediation, driverless car, epigenetics, failed state, financial innovation, Firefox, food miles, Ford Model T, future of work, Future Shock, global pandemic, global supply chain, global village, hive mind, hobby farmer, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, lateral thinking, linked data, low cost airline, low skilled workers, M-Pesa, mass immigration, Northern Rock, Paradox of Choice, peak oil, pensions crisis, precautionary principle, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, synthetic biology, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, Virgin Galactic, white flight, women in the workforce, work culture , Zipcar

Interestingly, the technology does not need any power to display the type unless the “page” is turned, so up to 20 books can be read before the reader needs recharging. I’d expect Apple to launch something similar, not least because the iTunes music-store model could easily be adapted to e-books rather than the current audiobooks. Prediction marketing Given how much the advertising industry goes on about creativity and strategy, it’s ironic that the big agencies have been so slow to embrace the brave new world of digital media. Perhaps this is because many prefer to kid themselves that they are in the movie business, or maybe many are still in denial about losing the strategic high ground to management consultancies.

Localization will also involve placing “real” car ads inside virtual street-racing games when your virtual car is off the road, or triggering a short animation on a washing-powder pack when you pass by the packs in the supermarket and they recognize you as a lapsed user. Call this “now marketing” or prediction marketing if you like. However, it would be a mistake to assume that the internet will take over from old media entirely. The internet is primarily a place where people go to find information or entertainment, or other like-minded individuals. This means that advertising will be repackaged to look like information or entertainment and it will be used to facilitate conversations between the people who know about things (such as brands) and the people who don’t.

Workforces will also become more balanced. There will be a greater spread of ages, more ethnic diversity and more women in the workforce, the latter significantly contributing to a shift away from the white middle-aged alpha male culture that has Work and Business 277 been dominant for so long. Decisions will be made using prediction markets and innovation will be run using open or distributed innovation principles. Work/life balance Instead of working less and enjoying a leisure society, we are working more. We are also commuting for longer periods. Being busy is a modern mark of prestige. This will all change. The open-all-hours work culture will be challenged by parents seeking more time with their kids and there will be law suits and regulation concerning the social costs of long work hours.


pages: 289 words: 95,046

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

Federal Reserve has been addicted to blowing bubbles for decades, creating dry tinder for crash after crash. Neither Spitznagel nor Taleb claim to know when the crashes will happen. As Spitznagel wrote in a 2020 letter to investors, “There are no crystal balls!” Not everyone agrees that it is impossible to predict market crashes, however. A growing breed of mathematicians, many of whom are steeped in a fiendishly arcane branch of science called complexity theory, practiced by brainiacs such as Taleb’s friend Yaneer Bar-Yam, have claimed they can detect certain signals in the market’s noise that auger collapse.

Sornette was predicting a much bigger crash, one that would have delivered a 10,000 percent gain. They held on, waiting for more turmoil. It didn’t happen. In fact, the market rallied the following day and soon the freak crash of October 27 was a distant memory. Sornette subsequently discovered that he could also apply his method to predict market rallies off of bear-market lows, what he called an “anti-bubble” of irrationally low prices. In January 1999, he forecast that the Nikkei index would soon recover from some fourteen years of doldrums, rebounding by 50 percent by the end of that year—which it did. His financial research culminated in the 2003 book Why Stock Markets Crash: Critical Events in Complex Financial Systems.

Referring to the dot-com bubble, he said in an August 2002 speech in Jackson Hole, Wyoming: “As events evolved, we recognized that, despite our suspicions, it was very difficult to definitely identify a bubble until after the fact—that is, when its bursting confirmed its existence.” Wall Street has always been rife with visionaries claiming to have discovered hidden patterns in the market’s warp and weft. The Elliott wave principle, propagated in the early twentieth century by accountant Ralph Nelson Elliott, claimed to predict market cycles and trends by pinpointing extremes in prices and investor psychology. Such cycles, the theory goes, move in discernible waves that slosh up and down—bubbles and crashes. Sornette himself has said that his model in ways parallels Elliott waves. But while technical analysis had at times enjoyed short-term success, over the long run there was little evidence investors could use it to accurately predict the market.


pages: 272 words: 64,626

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs by Andy Kessler

23andMe, Abraham Maslow, Alan Greenspan, Andy Kessler, bank run, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bob Noyce, bread and circuses, British Empire, business cycle, business process, California gold rush, carbon credits, carbon footprint, Cass Sunstein, cloud computing, collateralized debt obligation, collective bargaining, commoditize, computer age, Cornelius Vanderbilt, creative destruction, disintermediation, Douglas Engelbart, Dutch auction, Eugene Fama: efficient market hypothesis, fiat currency, Firefox, Fractional reserve banking, George Gilder, Gordon Gekko, greed is good, income inequality, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, Kickstarter, knowledge economy, knowledge worker, Larry Ellison, libertarian paternalism, low skilled workers, Mark Zuckerberg, McMansion, Michael Milken, Money creation, Netflix Prize, packet switching, personalized medicine, pets.com, prediction markets, pre–internet, profit motive, race to the bottom, Richard Thaler, risk tolerance, risk-adjusted returns, Silicon Valley, six sigma, Skype, social graph, Steve Jobs, The Wealth of Nations by Adam Smith, transcontinental railway, transfer pricing, vertical integration, wealth creators, Yogi Berra

But this is flawed in that people, not the market, set the actual cap or maximum amount of emissions, creating an almost artificial market for extra carbon credits to trade until the right price is set. The more human intervention overriding the market, the tougher true price discovery becomes. Lots of companies are playing around with prediction markets, using the Intelligence at the Edge and running surveys and varying pricing to figure out what people will pay for new products. Like it or not, markets will be inserted into more and more of our lives and Free Radicals ought to be the ones doing it, driving markets, making them happen everywhere.

Duke Power Grove, Andy Guns, Germs, and Steel (Diamond) Halstead, Maurice Halstead length Haves and Have-nots Health care the edge in personalized medicine Hedge funds, abundance, finding for Helú, Carlos Slim Henne, Albert Hersov, Rob Hewlett-Packard Hierarchy of needs Hoff, Ted Horizontal integration benefits of computers and voice communication and global economy and innovation and intellectual property ownership meaning of and price United States example How Capitalism Saved America (DiLorenzo) How We Got Here (Kessler) Hulu Humans, adapting technology to Hybrid autos IBM, vertical integration ICQ instant messaging Imperialism Income, generational differences Industrialization, and specialization Innovation, and horizontal integration Instant messaging, virtual pipe of Insurance companies, as Thieves Integration, horizontal Intel Intellectual property and horizontal integration and price cuts Intelligence (IQ), parameters of Intelligence at the edge cloud computing in health care social networking Interest rates, and Fed Internet digitized products, lack of protection of evolution of horizontal layers peer to peer (P2P) virtual pipes See also Networks; specific companies Internet stocks Investment capital, money/highest returns connection iPad iPhone iPod iTunes Jenkins, Holman Jobs Creators eliminating with technology licensed occupations replacing with technology Servers Slackers Slimers Sloppers Sponges Super Sloppers Thieves Jobs, Steve Junk bonds Kamangar, Salar Katzenberg, Jeffrey Keynes, John Maynard Kindle Kittler, Fred Kluge, John Lawyers, as Sponges Lehman Brothers bankruptcy Licenses, employment-related LinkedIn Livingston, Robert Longshoremen, as Sloppers McCaw, Craig McKnight, Dr. Jerry McNary, Robert Malone, John Maps, Google Market entrepreneurs Vanderbilt as example as winners Marketers, as Super Sloppers Markets benefits of for information for politics prediction markets price discovery by stock markets Mashups Maslow, Abraham Media defined empires, building of relationship to virtual pipe versus technology Media companies, vertical integration of Medicine, personalized Memory, chunks Mickos, Mårten Microcosm (Gilder) Microsoft employee interviews at Microsoft Word Midgley, Thomas Milken, Michael Mirabilis Money supply classic formula filled bucket comparison gold standard Monopoly, Vanderbilt fight against Moore, Gordon Moore’s law Mozilla Foundation Murdoch, Rupert Mushet, Robert Music, digital piracy virtual pipes for MySpace MySQL Nantell, Jim Napoleon Napster Needs, hierarchy of Netflix recommendations to customers Netscape Networks cloud computing dumb edge of network, intelligence at social networking See also Internet 99% Conference Nintendo Novelists, compared to programmers Noyce, Bob Nudge (Thaler and Sunstein) Obama, Barack Obama, Michelle Ofoto Open-source software Oracle Organic foods Organizational charts Orman, Suze O’Rourke, P.

Outliers (Gladwell) Page, Larry Palm Pre Pandora Peer to Peer (P2P) Personal Genome Project Personalized technology, examples of Physicians, as Thieves Pipes. See Virtual pipe Piracy, of digital products Pirate Bay Political entrepreneurs becoming examples of failure of media moguls as operation of Sponges created by Prague Stock Exchange Prediction markets Prevailing wage laws Price, and horizontal integration Procter & Gamble Productivity defined jobs, eliminating with technology wealth accumulation with. See Productivity and wealth Productivity and wealth Creators of economic principles and efficiency and exceptionalism guaranteed profits and horizontal integration jobs, hierarchy of jobs, replacing with technology and money supply non-productive workers, types of workweek over time Profit as business driver.


pages: 477 words: 75,408

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

But for now, Ford's reading of the omens seems to me little better than fortune telling with entrails or tarot cards.” Having unburdened himself of this cynicism, Hanson proceeds to offer a constructive suggestion. He advocates forecasting by means of prediction markets, where people place bets on particular economic or policy outcomes, like the level of unemployment at some future date. He argues that prediction markets give us a financial stake in being accurate when we make forecasts, rather than just trying to look good to our peers. Tyler Cowen A professor at George Mason University and co-author of an extremely popular blog, Tyler Cowen was New Jersey's youngest ever chess champion.

We should be employing economists and others to monitor the available data for signs of technological unemployment, and devising new ways to detect it. The economist Robin Hanson thinks that machines will eventually render most humans unemployed, but that it will not happen for many decades, probably centuries. Despite this scepticism, he proposes an interesting way to watch out for the eventuality: prediction markets. People make their best estimates when they have some skin in the forecasting game. Offering people the opportunity to bet real money on when they see their own jobs or other peoples’ jobs being automated may be an effective way to improve our forecasting.[cccliv] Planning We don’t have sufficient information to draw up detailed plans for the way we would like our economies and societies to evolve.


pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backpropagation, backtesting, barriers to entry, behavioural economics, book value, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, data science, deep learning, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial intermediation, Flash crash, Geoffrey Hinton, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, low interest rates, machine readable, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, Performance of Mutual Funds in the Period, popular capitalism, prediction markets, price discovery process, profit motive, proprietary trading, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

What is the best way to deal with myriad shifting rules, all of them imperfect, many of them conflicting, based on different sets of circumstances and assumptions? Trading is a microcosm of reality, a dynamic environment of profound complexity in which millions of participants act and react based on rules and beliefs that in turn feed back into and affect the larger environment. The challenge in trading is to derive rules that describe and predict markets, then to use them successfully to earn profits, without changing those markets in ways that might result in the destruction of the rule itself. We represent trading rules as alphas, algorithms that seek to predict the future of securities returns. Managing millions of alphas, each reflecting some hypothesis about the markets, is a complicated matter and a subject unto itself.

Containing systematic biases, by contrast, requires a sustained commitment of time and technological sophistication to ferret out look-ahead bias and overfitting. 11 The Triple-Axis Plan By Nitish Maini The world of quantitative investing is growing extremely rapidly. Quants are developing new ways of predicting market fluctuations by using mathematics, computer programming, and an ever-proliferating array of datasets. Discovering new alphas can be a formidable challenge, however, particularly for new quants. An efficient exploration of the vast universe of possible alphas – what we call the alpha space – requires a structured strategy, an anchoring point, and an orientation technique, otherwise known as the Triple-Axis Plan (TAP).

The answer depends partly on the size and pricing power of firms to lower trading and borrowing costs as much as possible to mimic the investment bank setup. Typically, only the largest hedge fund firms or active managers with related broker-dealer entities had the potential ability to negotiate such advantageous deals. Some segments of the overall index arbitrage strategy, however, such as predicting market impact for certain indices, can be implemented by active managers without necessarily requiring a large balance sheet. MARKET IMPACT FROM INDEX CHANGES Besides being a popular ETF to short, the IWM fund is reconstituted annually, causing some market impact on its constituents and former constituents compared with other sought-after ETFs.


Trade Your Way to Financial Freedom by van K. Tharp

asset allocation, backtesting, book value, Bretton Woods, buy and hold, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, compound rate of return, computer age, distributed generation, diversification, dogs of the Dow, Elliott wave, high net worth, index fund, locking in a profit, margin call, market fundamentalism, Market Wizards by Jack D. Schwager, passive income, prediction markets, price stability, proprietary trading, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Ronald Reagan, Savings and loan crisis, Sharpe ratio, short selling, Tax Reform Act of 1986, transaction costs

THERE’S AN ORDER TO THE UNIVERSE The idea that there is an order to the universe is extremely popular. People want to understand how the markets work, so it is most appealing to them to find some underlying structure. They believe, of course, that once you know the underlying structure, you can predict market movements. In many cases, such theories are even more exact because they attempt to predict market turning points. This naturally appeals to the psychological bias that most people have of wanting to be right and to have control over the markets. As a result, they want to catch market turning points. In addition, it’s a highly marketable idea to sell to the public.

In contrast, low periods of sunspot activity seem to correlate with what might be termed declines in civilization. Obviously, if such a theory is valid and if sunspot activity is predictable, then one would expect sunspot activity to have a strong effect on what happens in the market. There are numerous attempts to correlate and predict markets based upon major physical systems such as the activity of the sun. It is very easy to put together enough best-case examples to prove to others—or yourself—that these theories are correct. I’ve seen it happen hundreds of times because there is a simple perceptual bias that will convince people of certain relationships from just a few well-chosen examples.

Furthermore, certain “magical” societies and sects seem to carry this notion forward. The work of W. D. Gann, as currently promoted by many of his followers, is based on mathematical orderliness. Mathematical orderliness theories make two key assumptions: (1) that certain numbers are more important than others in predicting market turning points, and (2) that these numbers are important both in terms of price levels and in terms of time (that is, when to expect a change in the market). For example, suppose you believed that 45, 50, 60, 66, 90, 100, 120, 135, 144, 618, and so on, were magic numbers. What you’d do is find “significant” tops or bottoms and apply these numbers to them—looking at both time and price.


pages: 349 words: 102,827

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum by Camila Russo

4chan, Airbnb, Alan Greenspan, algorithmic trading, altcoin, always be closing, Any sufficiently advanced technology is indistinguishable from magic, Asian financial crisis, Benchmark Capital, Big Tech, bitcoin, blockchain, Burning Man, Cambridge Analytica, Cody Wilson, crowdsourcing, cryptocurrency, distributed ledger, diversification, Dogecoin, Donald Trump, East Village, Ethereum, ethereum blockchain, Flash crash, Free Software Foundation, Google Glasses, Google Hangouts, hacker house, information security, initial coin offering, Internet of things, Mark Zuckerberg, Maui Hawaii, mobile money, new economy, non-fungible token, off-the-grid, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, prediction markets, QR code, reserve currency, RFC: Request For Comment, Richard Stallman, Robert Shiller, Sand Hill Road, Satoshi Nakamoto, semantic web, sharing economy, side project, Silicon Valley, Skype, slashdot, smart contracts, South of Market, San Francisco, the Cathedral and the Bazaar, the payments system, too big to fail, tulip mania, Turing complete, Two Sigma, Uber for X, Vitalik Buterin

Frustrated with the idea that Bitcoin was just digital gold, he joined efforts to build applications on top of the Bitcoin protocol. As is usual with crypto projects, he came together with like-minded coders scattered all over the globe but brought together on the internet. He came across academic papers that talked about decentralized prediction markets but found that nobody had tried to implement them. Joey loved that he could potentially build a parallel financial system where anyone could create derivatives contracts on essentially anything—from speculations on the price of gold to who would win the US presidential election. Unlike past centralized experiments, these couldn’t be shut down.

But then First Blood, an online gaming company selling tokens to be used as rewards within its games, capped its September 26 ICO at $5.5 million. The sale was over in five minutes. A company that was less than a year old was able to raise more than $1 million per minute. Ethereum’s crowdsale had taken about two months; the sale for Joey Krug’s prediction market Augur took about a month; DigixDAO, the project that wanted to tie tokens to gold bars, had its sale in less than a day; and First Blood’s took five minutes. They had all raised at least $5 million. During the second week of November, Taylor was ready to spend some extra time working on MyEtherWallet from her LA home.

Web 3 dream a reality: Gavin Wood, “The Last Blog Post,” Ethereum Foundation Blog, January 11, 2016, https://blog.ethereum.org/2016/01/11/last-blog-post/. 18: The First Dapps 1. Ethereum smart contracts: Jack Peterson, Joseph Krug, Micah Zoltu, Austin K. Williams, and Stephanie Alexander, “Augur: A Decentralized Oracle and Prediction Market Platform (v2.0),” November 1, 2019, https://www.augur.net/whitepaper.pdf. 2. he wrote on GitHub: Vitalik Buterin, “Standardized_Contract_APIs,” GitHub, June 23, 2015, https://github.com/ethereum/wiki/wiki/Standardized_Contract_APIs/499c882f3ec123537fc2fccd57eaa29e6032fe4a. 3. with their thoughts: Alex Van de Sande, “Let's talk about the coin standard,” Reddit, 2015, https://www.reddit.com/r/ethereum/comments/3n8fkn/lets_talk_about_the_coin_standard/. 4. issue being discussed: Fabian Vogelsteller, “ERC: Token standard #20,” GitHub, November 19, 2015, https://github.com/ethereum/EIPs/issues/20. 5. ended up implementing: Rune Christensen, “Introducing eDollar, the ultimate stablecoin built on Ethereum,” Reddit, 2015, https://www.reddit.com/r/ethereum/comments/30f98i/introducing_edollar_the_ultimate_stablecoin_built/. 6. companies to build on: Jeff Wilcke, “Homestead Release,” Ethereum Foundation Blog, February 29, 2016, https://blog.ethereum.org/2016/02/29/homestead-release/. 7. world had Ethereum: Gavin Andresen, “Bit-thereum,” GavinTech (blog), June 9, 2014, http://gavintech.blogspot.com/2014/06/bit-thereum.html. 19: The Magic Lock 1.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apollo 11, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, driverless car, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, machine readable, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, supply chain finance, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

Moreover, we no longer need to take on the challenging task of identifying the best person for the job. It doesn’t matter which person is smartest. A diverse mix best does the trick. The collective intelligence of a crowd emerges on many occasions, as explored thoroughly by James Surowiecki in his book The Wisdom of Crowds. Examples include: Prediction markets, wherein a group of people together estimate the prospects for a horse race, political event, or economic occurrence by way of placing bets (unfortunately, this adept forecasting method cannot usually scale to the domain of PA, in which thousands or millions of predictions are generated by a predictive model).

MultiCare Health System (four hospitals in Washington): Karen Minich-Pourshadi for HealthLeaders Media, “Hospital Data Mining Hits Paydirt,” HealthLeaders Media Online, November 29, 2010. www.healthleadersmedia.com/page-1/FIN-259479/Hospital-Data-Mining-Hits-Paydirt. Medical centers and healthcare providers: Customer Potential Management Marketing Group, “Predictive Market Segmentation in Healthcare: Increasing the Effectiveness of Disease Prevention and Early Intervention,” CMP White Paper, CiteSeerxB Online, January 14, 2002. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.756. Blue Cross Blue Shield of Tennessee: Rick Whiting, “Businesses Mine Data to Predict What Happens Next,” InformationWeek, May 29, 2006. www.informationweek.com/businesses-mine-data-to-predict-what-hap/188500520.

See crime prediction for law enforcement politics, PA for See also electoral politics Portrait Software Post hoc, ergo propter hoc Power of Habit: Why We Do What We Do in Life and Business (Duhigg) PragmaticTheory team prediction benefits of choosing what to predict collective obsession with future predictions good vs. bad limits of organizational learning and prediction, effects of and on about Data Effect, The Ensemble Effect, The Induction Effect, The Persuasion Effect, The Prediction Effect, The Prediction Effect, The prediction markets predictive analytics. See PA (predictive analytics) Predictive Analytics World (PAW) conferences predictive models defined marketing models overlearning and assuming response modeling response uplift modeling univariate vs. multivariate See also ensemble models predictive models, launching about action and decision making causality and deployment phase Elder’s success in going live machine learning and building observation and personalization and risks in uplift modeling predictive technology See also machine learning predictor variables pregnancy and birth, predicting customer pregnancy and buying behavior premature births prejudice, risk of PREMIER Bankcard privacy Google policies on insight vs. intrusion regarding predicted consumer data and profiling customers Progressive Insurance psychology emotions, cause and effect of Freud on emotions predictive analysis in schizophrenia, predicting psychopathy, predicting Psych (TV show) purchases, predicting Q Quadstone R Radcliffe, Nicholas Radica Games Ralph’s random forests Rebellion Research recency recidivism prediction for law enforcement recommendation systems Reed Elsevier reliability modeling REO Speedwagon (band) response modeling drawbacks of examples of targeted marketing with response rates response uplift modeling retail websites, behavior on retirement, health and Richmond (VA) Police Department Rio Salado Community College Riskprediction.org.uk risk management risk scores Risky Business (film) Robin, Leo Romney, Mitt Royal Astronomy Society R software Russell, Bertrand Rutter, Brad S Saaf, Randy safety and efficiency, PA for Safeway sales leads, predicting Salford Systems Salsburg, David Santa Cruz (CA) Police Department sarcasm, in reviews Sartre, Jean-Paul SAS satellites, predicting fault in satisficing Schamberg, Lisa schizophrenia, predicting Schlitz, Don Schmidt, Eric Schwartz, Ari Science magazine security levels, predicting self-driving cars Selfridge, Oliver Semisonic (band) sepsis, predicting Sessions, Roger Shakespeare, William Shaw, George Bernard Shearer, Colin shopping habits, predicting sickness, predicting Siegel, Eric silence, concept of Silver, Nate Simpsons, The (TV show) Siri Sisters of Mercy Health Systems small business credit risks Smarr, Larry smoking and smokers health problems and causation for motion disorders and social effect and quitting SNTMNT Sobel, David social computing social effect social media networks data glut on happiness as contagious on healthcare LinkedIn PA for spam filtering on Twitter viral tweets and posts on YouTube See also Facebook sociology, uplift modeling applications for SpaceShipOne spam filtering Spider-Man (film) sporting events, crime rates and sports cars Sprint SPSS staff behavior.


pages: 326 words: 106,053

The Wisdom of Crowds by James Surowiecki

Alan Greenspan, AltaVista, Andrei Shleifer, Apollo 13, asset allocation, behavioural economics, Cass Sunstein, classic study, congestion pricing, coronavirus, Daniel Kahneman / Amos Tversky, experimental economics, Frederick Winslow Taylor, George Akerlof, Great Leap Forward, Gregor Mendel, Howard Rheingold, I think there is a world market for maybe five computers, interchangeable parts, Jeff Bezos, John Bogle, John Meriwether, Joseph Schumpeter, knowledge economy, lone genius, Long Term Capital Management, market bubble, market clearing, market design, Monkeys Reject Unequal Pay, moral hazard, Myron Scholes, new economy, offshore financial centre, Picturephone, prediction markets, profit maximization, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Robert Shiller, Ronald Coase, Ronald Reagan, seminal paper, shareholder value, short selling, Silicon Valley, South Sea Bubble, tacit knowledge, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Toyota Production System, transaction costs, ultimatum game, vertical integration, world market for maybe five computers, Yogi Berra, zero-sum game

Neumann, and Jack Wright, “Anatomy of an Experimental Political Stock Market,” American Economic Review 82 (1992): 1142–61; and Joyce Berg, Forrest Nelson, and Thomas Rietz, “Accuracy and Forecast Standard Error of Prediction Markets,” Tippie College of Business mimeo (2001), http://www.biz.uiowa.edu/iem/archive/forecasting.pdf. The IEM Web site has a page with links to research into the potential efficacy of prediction markets: http://www.biz.uiowa.edu/iem/archive/references.html. One important thing to note is that most of these papers, while they document the relative accuracy of the IEM’s forecasts, offer a different explanation for that accuracy than the one I offer here.

NetExchange was careful to make clear that the goal of the market would not be to predict terrorist incidents but rather to forecast broader economic, social, and military trends in the region. So perhaps the promise of PAM will actually get tested against reality, instead of being dismissed out of hand. It also seems plausible, and even likely, that the U.S. intelligence community will eventually return to the idea of using internal prediction markets—limited to analysts and experts—as a means of aggregating dispersed pieces of information and turning them into coherent forecasts and policy recommendations. Perhaps that would mean that the CIA would be running what Senators Wyden and Dorgan scornfully called “a betting parlor.” But we know one thing about betting markets: they’re very good at predicting the future. 5.


pages: 187 words: 62,861

The Penguin and the Leviathan: How Cooperation Triumphs Over Self-Interest by Yochai Benkler

Abraham Maslow, Alan Greenspan, behavioural economics, business process, California gold rush, citizen journalism, classic study, Daniel Kahneman / Amos Tversky, do well by doing good, East Village, Everything should be made as simple as possible, experimental economics, experimental subject, framing effect, Garrett Hardin, informal economy, invisible hand, jimmy wales, job satisfaction, Joseph Schumpeter, Kaizen: continuous improvement, Kenneth Arrow, knowledge economy, laissez-faire capitalism, loss aversion, Murray Gell-Mann, Nicholas Carr, peer-to-peer, prediction markets, Richard Stallman, scientific management, Scientific racism, Silicon Valley, social contagion, Steven Pinker, telemarketer, Toyota Production System, Tragedy of the Commons, twin studies, ultimatum game, Washington Consensus, Yochai Benkler, zero-sum game, Zipcar

Because talk is supposedly “cheap” in this sense—it doesn’t commit you to anything—a whole strand of work in economics has developed to place people in situations where they have to actually act, rather than talk: buy or not buy, invest or not invest, as this is assumed to be the only way to reveal their true preferences. That’s what prediction markets, which have become so popular in the run-up to elections, for example, do; prediction markets are speculative markets—betting exchanges—created to make predictions. They are just one example of a field called “mechanism design,” built on the assumption that it is not what people say, but what they do, that counts. It has been so influential that it won its inventors the Nobel Prize in economics in 2007.


pages: 240 words: 65,363

Think Like a Freak by Steven D. Levitt, Stephen J. Dubner

Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, behavioural economics, call centre, carbon credits, Cass Sunstein, colonial rule, Donald Shoup, driverless car, Edward Glaeser, Everything should be made as simple as possible, fail fast, food miles, gamification, Gary Taubes, Helicobacter pylori, income inequality, information security, Internet Archive, Isaac Newton, medical residency, Metcalfe’s law, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, sunk-cost fallacy, Tony Hsieh, transatlantic slave trade, Wayback Machine, éminence grise

All the team leaders were then asked to pick one of three signals—a green light, yellow light, or red light—to indicate their confidence in an on-time opening. All seven of them picked the green light. Great news! As it happens, this firm had also set up an internal prediction market, where any employee could anonymously place a small bet on various company directives. One bet asked whether the Chinese store would open on time. Considering that all seven team leaders had given it a green light, you might expect bettors to be similarly bullish. They weren’t. The prediction market showed a 92 percent chance the store wouldn’t open on time. Guess who was right—the anonymous bettors or the team leaders who had to stand in front of their bosses?


pages: 233 words: 66,446

Bitcoin: The Future of Money? by Dominic Frisby

3D printing, Alan Greenspan, altcoin, bank run, banking crisis, banks create money, barriers to entry, bitcoin, Bitcoin Ponzi scheme, blockchain, capital controls, Chelsea Manning, cloud computing, computer age, cryptocurrency, disintermediation, Dogecoin, Ethereum, ethereum blockchain, fiat currency, financial engineering, fixed income, friendly fire, game design, Hacker News, hype cycle, Isaac Newton, John Gilmore, Julian Assange, land value tax, litecoin, low interest rates, M-Pesa, mobile money, Money creation, money: store of value / unit of account / medium of exchange, Occupy movement, Peter Thiel, Ponzi scheme, prediction markets, price stability, printed gun, QR code, quantitative easing, railway mania, Ronald Reagan, Ross Ulbricht, Satoshi Nakamoto, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, Stephen Hawking, Steve Jobs, Ted Nelson, too big to fail, transaction costs, Turing complete, Twitter Arab Spring, Virgin Galactic, Vitalik Buterin, War on Poverty, web application, WikiLeaks

What we did with Ethereum is we kind of unified a lot of the 2.0 actors and put them into a big bucket and we’re building a completely new block chain and we’re building a completely new scripting language that basically adds in those missing features. ‘The end result is you can do things now like have Wall Street on a block chain. So any financial contract that you would see in Wall Street can now be put on a block chain just like money can. You can do prediction markets. You can have a Las Vegas gambling system living on a block chain. You can also take traditional server client internet apps like YouTube or Facebook or Netflix and you can actually now make all of these services run in a decentralized way with no central actor controlling them. You can do decentralized Dropbox, so instead of having all your files stored on a server; you can actually store them in a decentralized network and instead provide them with a token system just like Bitcoin.

Both the DVLA and the Land Registry badly need to pull their socks up, particularly the latter – 50% of land in the UK is still unregistered). This system of ownership and smart contracts has the potential to dramatically transform the legal system and slash costs. As we’ve suggested, insurance can be put on a block chain, prediction markets, and identity systems (username and password systems are on their way out), even services like Facebook, YouTube or Netflix. Why would you want YouTube, Facebook or Netflix running in a decentralized way with no central body in charge? It eliminates the problem of excessive personal information on Facebook, or your YouTube viewing habits being monitored and marketed to.


The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk by William J. Bernstein

asset allocation, backtesting, book value, buy and hold, capital asset pricing model, commoditize, computer age, correlation coefficient, currency risk, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, index arbitrage, index fund, intangible asset, John Bogle, junk bonds, Long Term Capital Management, p-value, passive investing, prediction markets, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, South Sea Bubble, stocks for the long run, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Wayback Machine, Yogi Berra, zero-coupon bond

I am continually amazed at the amount of time the financial and mass media devote to well-regarded analysts attempting to divine the movements of the market from political and economic events. This is a fool’s errand. Almost always these analysts are the employees of large brokerage houses; one would think that these organizations would tire of looking foolish on so regular a basis. (If you are not convinced of the futility of trying to predict market direction from economic conditions, then consider that the biggest money is made by buying when things look the bleakest: 1932, 1937, 1975, and 1982 were all great times to buy stocks. Then consider that the most dangerous times to buy or own stocks is when there is plenty of economic blue sky; those who bought in 1928, 1936, or 1966 were soon sorry.)

Prolonged market declines will make rebalancing seem a frustrating waste of money; in the end, however, asset prices almost always turn around, and you usually will be rewarded handsomely for your patience. 7. The markets are smarter than you are; they are also smarter than the experts. Remember that a stopped clock is right twice a day. Even the most inept analyst occasionally makes a good call, and he will probably be interviewed by Lou Rukeyser soon after he has made it. Nobody consistently predicts market direction. Very few money managers beat the market in the long run; those that have done so in the recent past are unlikely to do so in the future. Do not run with the crowd; those who follow the elephant herd often get dirty and squashed. 8. Know how expensive the tomatoes are. Keep an eye on market valuation.


Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, behavioural economics, Big Tech, Black Swan, blockchain, book value, business logic, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, deep learning, Donald Trump, driverless car, drop ship, Elon Musk, endowment effect, fail fast, fake news, future of work, Garrett Hardin, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, megaproject, meta-analysis, Nate Silver, nudge unit, Occam's razor, pattern recognition, pets.com, prediction markets, principal–agent problem, RAND corporation, randomized controlled trial, risk tolerance, Silicon Valley, SimCity, smart contracts, stem cell, sunk-cost fallacy, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, Tragedy of the Commons, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

If you are solving your problem solo, find a way to brainstorm your ideas with a diverse range of others, insiders and outsiders, at each stage of the process. Phillip Tetlock's work on forecasting shows that teams always outperform individuals in this form of problem solving, even very high‐performing solo practitioners. And the best superforecasting teams even beat crowd‐sourcing and prediction markets.5 Always try multiple trees/cleaves: Even when a problem seems perfectly designed for your favorite framework, try multiple cleaving frames to see what different questions and insights emerge. When we decided to look at obesity as a wicked problem, discussed in Chapter 9, we had a team meeting where we tried out various alternative‐cleaving frames.

Broaden your data sources: In every area of life, individual/workplace/society, there are core government and private data sets that everyone has access to. Sometimes these are terrific, but everyone has them, including your competitors, and there are often methodological issues in their collection. It is worth considering whether there are options for crowd sourcing alternative data, whether prediction markets cover your topic or could be induced to, or whether your interest area is amenable to some form of A|B testing or randomized controlled trials. Custom data collection costs have come down substantially and new data sets can yield insights very different from the obvious mainstream analyses.


pages: 252 words: 72,473

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

Affordable Care Act / Obamacare, Alan Greenspan, algorithmic bias, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, Cambridge Analytica, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, data science, disinformation, electronic logging device, Emanuel Derman, financial engineering, Financial Modelers Manifesto, Glass-Steagall Act, housing crisis, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Ida Tarbell, illegal immigration, Internet of things, late fees, low interest rates, machine readable, mass incarceration, medical bankruptcy, Moneyball by Michael Lewis explains big data, new economy, obamacare, Occupy movement, offshore financial centre, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price discrimination, quantitative hedge fund, Ralph Nader, RAND corporation, real-name policy, recommendation engine, Rubik’s Cube, Salesforce, Sharpe ratio, statistical model, tech worker, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor

Made in a Free World: Issie Lapowsky, “The Next Big Thing You Missed: Software That Helps Businesses Rid Their Supply Chains of Slave Labor,” Wired, February 3, 2015, www.​wired.​com/​2015/​02/​frdm/. Eckerd, a child and family services nonprofit: Darian Woods, “Who Will Seize the Child Abuse Prediction Market?,” Chronicle for Social Change, May 28, 2015, https://​chronicle​ofsocialchange.​org/​featured/​who-​will-​seize-​the-​child-​abuse-​prediction-​market/​10861. Boston Globe: Michael Levenson, “Can Analytics Help Fix the DCF?,” Boston Globe, November 7, 2015, www.​bostonglobe.​com/​2015/​11/​07/​childwelfare-​bostonglobe-​com/​AZ2kZ7ziiP8c​BMOite2KKP/story.​html. ABOUT THE AUTHOR Cathy O’Neil is a data scientist and the author of the blog mathbabe.​org.


pages: 434 words: 77,974

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts by Lorne Lantz, Daniel Cawrey

air gap, altcoin, Amazon Web Services, barriers to entry, bitcoin, blockchain, business logic, business process, call centre, capital controls, cloud computing, corporate governance, creative destruction, cross-border payments, cryptocurrency, currency peg, disinformation, disintermediation, distributed ledger, Dogecoin, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, global reserve currency, information security, initial coin offering, Internet of things, Kubernetes, litecoin, low interest rates, Lyft, machine readable, margin call, MITM: man-in-the-middle, multilevel marketing, Network effects, offshore financial centre, OSI model, packet switching, peer-to-peer, Ponzi scheme, prediction markets, QR code, ransomware, regulatory arbitrage, rent-seeking, reserve currency, Robinhood: mobile stock trading app, Ross Ulbricht, Satoshi Nakamoto, Silicon Valley, Skype, smart contracts, software as a service, Steve Wozniak, tulip mania, uber lyft, unbanked and underbanked, underbanked, Vitalik Buterin, web application, WebSocket, WikiLeaks

A Swiss-based nonprofit foundation was created to initiate the ICO, and starting in July 2014, for 42 days Ethereum conducted a crowdsale. Approximately 60 million ether tokens were sold, raising some 31,000 BTC (around $18 million at the time). This became the template for many other ICOs in the future. Gnosis A decentralized prediction market platform, Gnosis shares some concepts and some personnel with the Augur project, an early Ethereum-based offering that had its ICO in 2015. The Gnosis multisignature wallet is still one of the most widely used in the Ethereum ecosystem, especially for applications such as cold storage of tokens.

Projects with long-term viable token economies are usually able to articulately answer the following questions: Why does this product/service have to be on a blockchain? Why can’t you provide the same thing through a centralized database? Most token projects cannot answer this question well and therefore have poor token economics. An example of a product/service that has to be on a blockchain is the Augur token (REP). Augur is a prediction market that requires regulatory scrutiny in many jurisdictions. If Augur were run on a centralized server, there would be increased capability to shut down the service by seizing the server. Note Motivations for those running an ICO are often not the same as for those running or investing in a venture capital–backed startup company.


The Disciplined Trader: Developing Winning Attitudes by Mark Douglas

Albert Einstein, conceptual framework, fear of failure, financial independence, prediction markets, risk tolerance, the market place

Many traders will try to circumvent confronting these limiting beliefs by becoming an expert market analyst. It doesn't matter how good a market analyst you become; if you don't release yourself from the effects of these beliefs, you won't be successful to the extent these limiting beliefs have power in your mental system. There are many market gurus who can predict market moves with uncanny accuracy but can't make money as a trader. Either they don't know the nature of beliefs and how they affect and determine behavior, or they don't want to confront the issues surrounding these beliefs. You have to want to do it or nothing will happen. And if you choose not to, you will be subjecting yourself to the same recurring cycles of negative experiences again and again until you either decide to work through whatever issues are necessary or lose all your money and have to give it up.

You will experience that past regardless of the opportunities the environment may have to offer in that same moment. Your individual history will repeat itself until you change your history, which will then allow you to learn and experience something new. Evolving beyond your fears is also the best way to learn how to predict market behavior. The more fearful traders are, the fewer the choices they perceive as available to themselves and the easier it is to predict their behavior. You will be able to recognize this clearly in others when you recognize it yourself and work your way out of the condition where you trade with fear.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, behavioural economics, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, data science, David Heinemeier Hansson, deep learning, disruptive innovation, driverless car, Dunbar number, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, gamification, Google X / Alphabet X, haute couture, helicopter parent, hype cycle, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Mary Meeker, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, planned obsolescence, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, scientific management, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, subscription business, survivorship bias, The Home Computer Revolution, the long tail, too big to fail, US Airways Flight 1549, vertical integration, web application, zero-sum game

The market doesn’t care when you finished school Getting your digital on It’s not my job Welcome to med school Note Chapter 5: Rehumanisation: words define our future Dead-end products A future of unfinished products The malleable marketplace Corporate skulduggery? Outsourcing logic The end of bison hunting The tastemakers The selfish era So how do we survive? Creative types Collaboration, creative orientation and counter intuition Note Chapter 6: Demographics is history: moving on from predictive marketing How to get profiled The price of pop culture The best average The weapon of choice Don’t fence me in How do you define a teenager? Stealing music or connecting? Marketing 1.0 Marketing revised The new intersection Social + interests = intention The story of cities Do I know you?

Corporations have to start acting more like people and less like evil organisms with their own agenda. Note 1 Driverless car prediction dates by various respected auto and technology firms: www.driverless-future.com/?page_id=384 CHAPTER 6 Demographics is history: moving on from predictive marketing If you learned your marketing trade any time in the past 60 years, there’s a very good chance a large part of what you learned was related to demographic profiling, the statistical art of putting people into behaviour buckets. These were clusters created to define what people believe and how they’re likely to behave so that they could be ‘targeted’ with financial efficiency.


pages: 205 words: 18,208

The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom? by David Brin

affirmative action, airport security, Ayatollah Khomeini, clean water, cognitive dissonance, corporate governance, data acquisition, death of newspapers, Extropian, Garrett Hardin, Howard Rheingold, illegal immigration, informal economy, information asymmetry, information security, Iridium satellite, Jaron Lanier, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Kevin Kelly, Marshall McLuhan, means of production, mutually assured destruction, Neal Stephenson, offshore financial centre, Oklahoma City bombing, open economy, packet switching, pattern recognition, pirate software, placebo effect, plutocrats, prediction markets, Ralph Nader, RAND corporation, Robert Bork, Saturday Night Live, Search for Extraterrestrial Intelligence, Steve Jobs, Steven Levy, Stewart Brand, telepresence, The Turner Diaries, Timothy McVeigh, trade route, Tragedy of the Commons, UUNET, Vannevar Bush, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, workplace surveillance , Yogi Berra, zero-sum game, Zimmermann PGP

.: Addison-Wesley, 1994), and Edward Tenner. Why Things Bite Back (New York: Knopf, 1996). 267 ... a “predictions market” ... University of California economist Robin Hanson calls his system a “betting pool on disputed science questions, where the current odds-on favorites are treated as the current intellectual consensus. Ideas futures markets let you bet on the future settlement of a scientific controversy. [See http://www.ideosphere.com/ and http://hanson.berkeley.edulideafutures.html.] But the method may have wider applications.” Note: a form of predictions market was depicted in John Brunnerʼs wonderfully prescient science fiction novel The Shockwave Rider in 1974, a work that illustrated principles of transparency and also invented the terminology, possibly the very concepts, of computer “viruses” and “worms.” 267 ...

In the 1970s, efforts were made to catalog predictions using the crude technique of mailing postcards to a post office box in New York City, but sorting through shoe boxes did not prove an efficient or comprehensive method of correlating results, and the effort collapsed. The Internet has changed all that. For example, a “predictions market” has been set up by Robin Hanson, a researcher at the University of California at Berkeley. In his Web space, visitors bet against each other about future trends in science, much like Vegas odds makers, or gamblers on the Chicago commodities exchange. Winners are those whose guesses (or sage insights) prove correct most often.


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, behavioural economics, buy and hold, buy low sell high, cryptocurrency, data science, deep learning, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, OpenAI, p-value, paper trading, performance metric, prediction markets, proprietary trading, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

This section comprises the following chapters: Chapter 4, Classical Trading Strategies Driven by Human Intuition Chapter 5, Sophisticated Algorithmic Strategies Chapter 6, Managing Risk in Algorithmic Strategies Classical Trading Strategies Driven by Human Intuition During the previous chapters, we used statistical methods to predict market price movement from historical data. You may think that you know how to manipulate data, but how can these statistical techniques be applied to real trading? After spending so much time working on data, you may also want to know some key trading strategies that you can apply to make money. In this chapter, we will talk about basic algorithmic strategies that follow human intuition.

To summarize, since market participants are continuously evolving and new participants enter the market and existing participants leave the market, it is possible for us to lose those participants that provide the trading signals that we use in our trading strategies. To deal with this, we have to constantly search for new trading signals and diversify trading signals and strategies to capture more market participants' intentions and predict market price moves. Profit decay due to discovery by other participants We discussed the possibility of and the impact of other market participants discovering our trading signals and using the same signals that our trading strategies utilize to make money. Similar to other market participants discovering the same trading signals that our trading strategies use and hurting our profitability, it is possible for other market participants to discover our order flow and strategy behavior and then find ways to anticipate and leverage our trading strategy's order flow to trade against us in a way that causes our trading strategies to lose money.


pages: 135 words: 26,407

How to DeFi by Coingecko, Darren Lau, Sze Jin Teh, Kristian Kho, Erina Azmi, Tm Lee, Bobby Ong

algorithmic trading, asset allocation, Bernie Madoff, bitcoin, blockchain, buy and hold, capital controls, collapse of Lehman Brothers, cryptocurrency, distributed ledger, diversification, Ethereum, ethereum blockchain, fiat currency, Firefox, information retrieval, litecoin, margin call, new economy, passive income, payday loans, peer-to-peer, prediction markets, QR code, reserve currency, robo advisor, smart contracts, tulip mania, two-sided market

Appendix CoinGecko's Recommended DeFi Resources Information DeFi Prime - https://defiprime.com/ DeFi Pulse - https://defipulse.com/ DeFi Tutorials - https://defitutorials.com/ LoanScan - http://loanscan.io/ Newsletters Bankless - https://bankless.substack.com/ DeFi Tutorials - https://defitutorials.substack.com/ DeFi Weekly - https://defiweekly.substack.com/ Dose of DeFi - https://doseofdefi.substack.com/ Ethhub - https://ethhub.substack.com/ My Two Gwei - https://mytwogwei.substack.com/ The Defiant - https://thedefiant.substack.com/ Week in Ethereum News - https://www.weekinethereumnews.com/ Podcast BlockCrunch - https://castbox.fm/channel/Blockcrunch%3A-Crypto-Deep-Dives-id1182347 Chain Reaction - https://fiftyonepercent.podbean.com/ Into the Ether - Ethhub - https://podcast.ethhub.io/ PoV Crypto - https://povcryptopod.libsyn.com/ Wyre Podcast - https://blog.sendwyre.com/wyretalks/home Youtube Chris Blec - https://www.youtube.com/c/chrisblec Bankless Level-Up Guide https://bankless.substack.com/p/bankless-level-up-guide Projects We Like Too Dashboard Interfaces DeFi Prime Portfolio - http://portfolio.defiprime.com Frontier - https://frontierwallet.com/ InstaDApp - https://instadapp.io/ MyDeFi - https://mydefi.org/apps Zerion - https://zerion.io/ Decentralized Exchanges Bancor - https://www.bancor.network/ Curve Finance - https://www.curve.fi/ Dex Blue https://dex.blue/ Kyber - https://kyberswap.com/swap Exchange Aggregators 1inch - https://1inch.exchange/ Dex.ag - https://dex.ag/ Paraswap - https://paraswap.io/ Lending and Borrowing Dharma - https://www.dharma.io/ Prediction Markets Augur - https://www.augur.net/ Taxes TokenTax - https://tokentax.co/ Wallet GnosisSafe - https://safe.gnosis.io/ Monolith - https://monolith.xyz/ Yield Optimisers Iearn - https://iearn.finance/ RAY - https://staked.us/v/robo-advisor-yield/ References Chapter 1: Traditional Financial Institutions Bagnall, E. (2019, June 30).


Principles of Corporate Finance by Richard A. Brealey, Stewart C. Myers, Franklin Allen

3Com Palm IPO, accelerated depreciation, accounting loophole / creative accounting, Airbus A320, Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, banking crisis, Bear Stearns, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, Boeing 747, book value, break the buck, Brownian motion, business cycle, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, capital controls, Carl Icahn, Carmen Reinhart, carried interest, collateralized debt obligation, compound rate of return, computerized trading, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, cross-subsidies, currency risk, discounted cash flows, disintermediation, diversified portfolio, Dutch auction, equity premium, equity risk premium, eurozone crisis, fear index, financial engineering, financial innovation, financial intermediation, fixed income, frictionless, fudge factor, German hyperinflation, implied volatility, index fund, information asymmetry, intangible asset, interest rate swap, inventory management, Iridium satellite, James Webb Space Telescope, junk bonds, Kenneth Rogoff, Larry Ellison, law of one price, linear programming, Livingstone, I presume, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, market bubble, market friction, money market fund, moral hazard, Myron Scholes, new economy, Nick Leeson, Northern Rock, offshore financial centre, PalmPilot, Ponzi scheme, prediction markets, price discrimination, principal–agent problem, profit maximization, purchasing power parity, QR code, quantitative trading / quantitative finance, random walk, Real Time Gross Settlement, risk free rate, risk tolerance, risk/return, Robert Shiller, Scaled Composites, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, Skype, SpaceShipOne, Steve Jobs, subprime mortgage crisis, sunk-cost fallacy, systematic bias, Tax Reform Act of 1986, The Nature of the Firm, the payments system, the rule of 72, time value of money, too big to fail, transaction costs, University of East Anglia, urban renewal, VA Linux, value at risk, Vanguard fund, vertical integration, yield curve, zero-coupon bond, zero-sum game, Zipcar

The nearby box shows how market prices reveal opinions about issues as diverse as a presidential election, the weather, or the demand for a new product. FINANCE IN PRACTICE ● ● ● ● ● Prediction Markets Stock markets allow investors to bet on their favourite stocks. Prediction markets allow them to bet on almost anything else. These markets reveal the collective guess of traders on issues as diverse as New York City snowfall, an avian flu outbreak, and the occurrence of a major earthquake. Prediction markets are conducted on the majorfutures exchanges and on a number of smaller online exchanges such as Intrade (www.intrade.com) and the Iowa Electronic Markets (www.biz.uiowa.edu/iem).

Investors, it seemed, had become increasingly pessimistic about the currency’s prospects. Participants in prediction markets are putting their money where their mouth is. So the forecasting accuracy of these markets compares favorably with those of major polls. Some businesses have also formed internal prediction markets to survey the views of their staff. For example, Google operates an internal market to forecast product launch dates, the number of Gmail users, and other strategic questions.* *Google’s experience is analyzed in B. Cowgill, J. Wolfers, and E. Zitzewitz, “Using Prediction Markets to Track Information Flows: Evidence from Google,” Working paper, Dartmouth College, January 2009 Lesson 4: There Are No Financial Illusions In an efficient market there are no financial illusions.

., 509n Fisher, Irving, 62–63, 699n Fitch credit ratings, 65–66, 595, 628n Fitzpatrick, Dan, 4n Fixed-income market, 47 Fixed-rate debt, 357 Flat (clean) price, 48n FleetBoston, 812 Fleet Financial Group, 812 Floating charge, 626 Floating-price convertibles, 622 Floating-rate debt, 357, 588, 588n, 673–675 Floating-rate notes, 608 Floating-rate preferred stock, 797, 797n Floor, 608 Flow-to-equity method of valuing companies, 488 Forcing conversion, 618–619 Ford, Henry, 865, 865n Ford Credit, 472, 628 Ford Motor Company, 37, 172, 178–179, 194, 198, 302, 471, 472, 628, 712, 810, 865 Forecasts cash flow, 24–25, 107–108, 138, 488 earnings per share, 312–313 economic rents in, 278–288 market values in, 273–278 prediction markets, 338 Foreign bonds, 616–617 Foreign Credit Insurance Association (FCIA), 786 Foreign exchange risk, 693–710 basic relationships, 695–704 economic exposure, 705–706 foreign exchange market, 693–710 hedging currency risk, 704–706 international investment decisions, 706–710 transaction exposure, 705–706 Forelle, C., 551n Fortis, 367 Fortune Brands, 842 Forward contracts, 665 forward exchange rates, 694–695 forward market, defined, 694 forward rate agreements (FRAs), 672 homemade, 672 simple, 665 speculation in, 681–683 Forward exchange rates, 694–695 Forward interest rate, 672 Forward premium, 697 Forward prices, 665 Forward rate, expectations theory of, 699–700 Forward rate agreements (FRAs), 672 Forward rate of interest, 58n France compounding intervals in, 46–47 corporations in, 5n government bond valuation, 46–47 nominal versus real exchange rates, 701–703 ownership and control in, 869 Frank, M., 470n Franks, J.


pages: 348 words: 97,277

The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey

3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, blockchain, blood diamond, Blythe Masters, business process, buy and hold, carbon credits, carbon footprint, cashless society, circular economy, cloud computing, computer age, computerized trading, conceptual framework, content marketing, Credit Default Swap, cross-border payments, crowdsourcing, cryptocurrency, cyber-physical system, decentralized internet, dematerialisation, disinformation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Dunbar number, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fake news, fault tolerance, fiat currency, financial engineering, financial innovation, financial intermediation, Garrett Hardin, global supply chain, Hernando de Soto, hive mind, informal economy, information security, initial coin offering, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, post-truth, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, Project Xanadu, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, Tragedy of the Commons, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, Vitalik Buterin, web of trust, work culture , zero-sum game

The sense that something extraordinary had been unleashed was cemented in November 2016, when a site called Golem, which offered a platform for trading idle computer power (it billed itself as the “Airbnb for computers”), raised $8.6 million in half an hour. After that, money seemed to open up for anyone with a white paper and a token to sell. An initial high-water mark came in April 2017 when Gnosis, whose platform allows users to create prediction markets for betting on just about anything, sold 5 percent of the tokens created by the company to raise $12.5 million in twelve minutes. With the other 95 percent controlled by the founders, those prices meant that the implied valuation of the entire enterprise stood at $300 million—a figure that soon rose above $1 billion as the Gnosis token promptly quadrupled in price in the secondary market.

In an age when U.S. presidents peddle “alternative facts” and pundits talk openly about our “post-truth society,” using the truth machine to put a value on honesty sounds appealing. Already, the blockchain startup Augur is exploring these ideas. The firm has built a decentralized, cryptocurrency-based prediction market on top of Ethereum, where players place bets on an outcome of some event or other, the result of which depends on confirmation by certain individuals. Those confirming parties will bet their rep tokens that they are telling the truth, and if a majority agrees that they are, the system returns the tokens and pays them in cash.


pages: 343 words: 102,846

Trees on Mars: Our Obsession With the Future by Hal Niedzviecki

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Robotics, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Charles Babbage, Colonization of Mars, computer age, crowdsourcing, data science, David Brooks, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Flynn Effect, Ford Model T, Future Shock, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, independent contractor, Internet of things, invention of movable type, Jaron Lanier, Jeff Bezos, job automation, John von Neumann, knowledge economy, Kodak vs Instagram, life extension, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Neil Armstrong, One Laptop per Child (OLPC), Peter H. Diamandis: Planetary Resources, Peter Thiel, Pierre-Simon Laplace, Ponzi scheme, precariat, prediction markets, Ralph Nader, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, rising living standards, Robert Solow, Ronald Reagan, Salesforce, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, tech worker, technological singularity, technological solutionism, technoutopianism, Ted Kaczynski, TED Talk, Thomas L Friedman, Tyler Cowen, Uber and Lyft, uber lyft, Virgin Galactic, warehouse robotics, working poor

The big surprise has been the support for the unabashedly elitist ‘super-forecaster’ hypothesis. . . . When we randomly assigned ‘supers’ into elite teams, they blew the lid off IARPA’s performance goals. They beat the unweighted average (wisdom-of-overall-crowd) by 65 percent; beat the best algorithms of four competitor institutions by 35-60 percent; and beat two prediction markets by 20-35 percent.”8 Around the same time, Ignatius of the Washington Post made the (unattributed) claim that “the top forecasters, drawn from universities and elsewhere, performed about 30 percent better than the average for intelligence community analysts who could read intercepts and other secret data.”9 In other words, according to Ignatius, intelligence analysts with security clearance to read every secret report and see every drone image were still considerably worse at prediction than Tetlock’s best performers.

IARPA and Philip Tetlock’s network of collaborators aren’t the only group trying to incorporate big data and psychology and crowdsourcing to get us to the point where we can actually turn future into information. In January 2014, George Mason University in Virginia launched SciCast, billed as the “largest and most advanced science and technology prediction market in the world.” I’m not going to get into all the technical details of how SciCast works, but basically the idea is that anyone in the world can join up and start answering questions about what will happen in the realms of science and technology in the next six months to two years. The more questions you answer correctly as time goes by, the higher your score.


pages: 519 words: 102,669

Programming Collective Intelligence by Toby Segaran

algorithmic management, always be closing, backpropagation, correlation coefficient, Debian, en.wikipedia.org, Firefox, full text search, functional programming, information retrieval, PageRank, prediction markets, recommendation engine, slashdot, social bookmarking, sparse data, Thomas Bayes, web application

Sites like Amazon and Netflix use information about the things people buy or rent to determine which people or items are similar to one another, and then make recommendations based on purchase history. Other sites like Pandora and Last.fm use your ratings of different bands and songs to create custom radio stations with music they think you will enjoy. Chapter 2 covers ways to build recommendation systems. Prediction markets are also a form of collective intelligence. One of the most well known of these is the Hollywood Stock Exchange (http://hsx.com), where people trade stocks on movies and movie stars. You can buy or sell a stock at the current price knowing that its ultimate value will be one millionth of the movie's actual opening box office number.

, Learning from Clicks, Learning from Clicks, Setting Up the Database, Feeding Forward, Training with Backpropagation, Training Test, Training Test artificial, Learning from Clicks, Learning from Clicks, Setting Up the Database, Feeding Forward, Training with Backpropagation, Training Test, Training Test backpropagation, Training with Backpropagation connecting to search engine, Training Test designing click-training network, Learning from Clicks feeding forward, Feeding Forward setting up database, Setting Up the Database training test, Training Test neural network classifier, Exercises neural networks, Neural Networks, Neural Networks, Neural Networks, Neural Networks, Training a Neural Network, Training a Neural Network, Training a Neural Network, Strengths and Weaknesses, Strengths and Weaknesses backpropagation, and, Training a Neural Network black box method, Strengths and Weaknesses combinations of words, and, Neural Networks multilayer perceptron network, Neural Networks strengths and weaknesses, Strengths and Weaknesses synapses, and, Neural Networks training, Training a Neural Network using code, Training a Neural Network news sources, A Corpus of News newsfeatures.py, Selecting Sources, Downloading Sources, Downloading Sources, Downloading Sources, Converting to a Matrix, Using NumPy, The Algorithm, Displaying the Results, Displaying the Results, Displaying by Article, Displaying by Article getarticlewords function, Downloading Sources makematrix function, Converting to a Matrix separatewords function, Downloading Sources shape function, The Algorithm showarticles function, Displaying the Results, Displaying by Article showfeatures function, Displaying the Results, Displaying by Article stripHTML function, Downloading Sources transpose function, Using NumPy nn.py, Setting Up the Database, Setting Up the Database, Setting Up the Database, Setting Up the Database searchnet class, Setting Up the Database, Setting Up the Database, Setting Up the Database, Setting Up the Database generatehiddennode function, Setting Up the Database getstrength method, Setting Up the Database setstrength method, Setting Up the Database nnmf.py, The Algorithm difcost function, The Algorithm non-negative matrix factorization (NMF), Supervised versus Unsupervised Learning, Clustering, Non-Negative Matrix Factorization, Non-Negative Matrix Factorization, Non-Negative Matrix Factorization, Using Your NMF Code factorization, Supervised versus Unsupervised Learning goal of, Non-Negative Matrix Factorization update rules, Non-Negative Matrix Factorization using code, Using Your NMF Code normalization, Normalization Function numerical predictions, Building Price Models numpredict.py, Building a Sample Dataset, Building a Sample Dataset, Defining Similarity, Defining Similarity, Defining Similarity, Defining Similarity, Subtraction Function, Subtraction Function, Weighted kNN, Weighted kNN, Cross-Validation, Cross-Validation, Cross-Validation, Heterogeneous Variables, Scaling Dimensions, Optimizing the Scale, Optimizing the Scale, Uneven Distributions, Estimating the Probability Density, Graphing the Probabilities, Graphing the Probabilities, Graphing the Probabilities createcostfunction function, Optimizing the Scale createhiddendataset function, Uneven Distributions crossvalidate function, Cross-Validation, Optimizing the Scale cumulativegraph function, Graphing the Probabilities distance function, Defining Similarity dividedata function, Cross-Validation euclidian function, Defining Similarity gaussian function, Weighted kNN getdistances function, Defining Similarity inverseweight function, Subtraction Function knnestimate function, Defining Similarity probabilitygraph function, Graphing the Probabilities probguess function, Estimating the Probability Density, Graphing the Probabilities rescale function, Scaling Dimensions subtractweight function, Subtraction Function testalgorithm function, Cross-Validation weightedknn function, Weighted kNN wineprice function, Building a Sample Dataset wineset1 function, Building a Sample Dataset wineset2 function, Heterogeneous Variables NumPy, Using NumPy, Using NumPy, Simple Usage Example, NumPy, Installation on Other Platforms, Installation on Other Platforms installation on other platforms, Installation on Other Platforms installation on Windows, Simple Usage Example usage example, Installation on Other Platforms using, Using NumPy O online technique, Strengths and Weaknesses Open Web APIs, Open APIs optimization, Optimization, Group Travel, Representing Solutions, Representing Solutions, Representing Solutions, Representing Solutions, The Cost Function, The Cost Function, The Cost Function, Random Searching, Hill Climbing, Simulated Annealing, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Optimizing for Preferences, Optimizing for Preferences, The Cost Function, The Cost Function, Network Visualization, Network Visualization, Counting Crossed Lines, Drawing the Network, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Optimizing the Scale, Exercises, Optimization, Optimization annealing starting points, Exercises cost function, The Cost Function, Optimization exercises, Exercises genetic algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms crossover or breeding, Genetic Algorithms generation, Genetic Algorithms mutation, Genetic Algorithms population, Genetic Algorithms genetic optimization stopping criteria, Exercises group travel cost function, Exercises group travel planning, Group Travel, Representing Solutions, Representing Solutions, Representing Solutions, The Cost Function, The Cost Function car rental period, The Cost Function departure time, Representing Solutions price, Representing Solutions time, Representing Solutions waiting time, The Cost Function hill climbing, Hill Climbing line angle penalization, Exercises network visualization, Network Visualization, Counting Crossed Lines, Drawing the Network counting crossed lines, Counting Crossed Lines drawing networks, Drawing the Network layout problem, Network Visualization network vizualization, Network Visualization pairing students, Exercises preferences, Optimizing for Preferences, Optimizing for Preferences, The Cost Function, The Cost Function cost function, The Cost Function running, The Cost Function student dorm, Optimizing for Preferences random searching, Random Searching representing solutions, Representing Solutions round-trip pricing, Exercises simulated annealing, Simulated Annealing where it may not work, Genetic Algorithms optimization.py, Group Travel, Representing Solutions, Representing Solutions, The Cost Function, Random Searching, Hill Climbing, Simulated Annealing, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Optimizing the Scale annealingoptimize function, Simulated Annealing geneticoptimize function, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms, Genetic Algorithms elite, Genetic Algorithms maxiter, Genetic Algorithms mutprob, Genetic Algorithms popsize, Genetic Algorithms getminutes function, Representing Solutions hillclimb function, Hill Climbing printschedule function, Representing Solutions randomoptimize function, Random Searching schedulecost function, The Cost Function P PageRank algorithm, Real-Life Examples, The PageRank Algorithm pairing students, Exercises Pandora, Real-Life Examples parse tree, Programs As Trees Pearson correlation, Hierarchical Clustering, Viewing Data in Two Dimensions hierarchical clustering, Hierarchical Clustering multidimensional scaling, Viewing Data in Two Dimensions Pearson correlation coefficient, Pearson Correlation Score, Pearson Correlation Coefficient, Pearson Correlation Coefficient code, Pearson Correlation Coefficient Pilgrim, Mark, Universal Feed Parser polynomial transformation, The Kernel Trick poplib, Exercises population, Genetic Algorithms, What Is Genetic Programming?, Creating the Initial Population, Genetic Algorithms diversity and, Creating the Initial Population Porter Stemmer, Finding the Words on a Page Pr(Document), Exercises prediction markets, Real-Life Examples price models, Building a Sample Dataset, Building a Sample Dataset, k-Nearest Neighbors, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises, Exercises building sample dataset, Building a Sample Dataset eliminating variables, Exercises exercises, Exercises item types, Exercises k-nearest neighbors (kNN), k-Nearest Neighbors laptop dataset, Exercises leave-one-out cross-validation, Exercises optimizing number of neighbors, Exercises search attributes, Exercises varying ss for graphing probability, Exercises probabilities, Calculating Probabilities, Starting with a Reasonable Guess, Probability of a Whole Document, A Quick Introduction to Bayes' Theorem, Combining the Probabilities, Graphing the Probabilities, Conditional Probability assumed probability, Starting with a Reasonable Guess Bayes' Theorem, A Quick Introduction to Bayes' Theorem combining, Combining the Probabilities conditional probability, Calculating Probabilities graphing, Graphing the Probabilities of entire document given classification, Probability of a Whole Document product marketing, Other Uses for Learning Algorithms public message boards, Filtering Spam pydelicious, Simple Usage Example, Simple Usage Example, pydelicious installation, Simple Usage Example usage example, Simple Usage Example pysqlite, Building the Index, Persisting the Trained Classifiers, Installation on All Platforms, Installation on All Platforms, pysqlite, Simple Usage Example importing, Persisting the Trained Classifiers installation on other platforms, Installation on All Platforms installation on Windows, Installation on All Platforms usage example, Simple Usage Example Python, Style of Examples, Python Tips advantages of, Style of Examples tips, Python Tips Python Imaging Library (PIL), Drawing the Dendrogram, Python Imaging Library, Installation on Windows, Installation on Windows, Installation on Windows installation on other platforms, Installation on Windows usage example, Installation on Windows Windows installation, Installation on Windows Python, genetic programming and, Programs As Trees, Programs As Trees, Representing Trees in Python, Building and Evaluating Trees, Displaying the Program building and evaluating trees, Building and Evaluating Trees displaying program, Displaying the Program representing trees, Representing Trees in Python traversing complete tree, Programs As Trees Q query layer, Design of a Click-Tracking Network querying, Querying, Querying query function, Querying R radial-basis function, The Kernel Trick random searching, Random Searching random-restart hill climbing, Hill Climbing ranking, What's in a Search Engine?


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

Alan Greenspan, Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Apollo 11, Asian financial crisis, bank run, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, butterfly effect, buy and hold, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, coastline paradox / Richardson effect, collateralized debt obligation, collective bargaining, currency risk, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, Financial Modelers Manifesto, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, Jim Simons, John Nash: game theory, junk bonds, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Market Wizards by Jack D. Schwager, martingale, Michael Milken, military-industrial complex, Myron Scholes, Neil Armstrong, new economy, Nixon triggered the end of the Bretton Woods system, Paul Lévy, Paul Samuelson, power law, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk-adjusted returns, Robert Gordon, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, Stuart Kauffman, The Chicago School, The Myth of the Rational Market, tulip mania, Vilfredo Pareto, volatility smile

In 1996, Sornette’s work on earthquakes earned him a part-time professor-in-residence position in UCLA’s earth and space sciences department, and at the Institute of Geophysics and Planetary Physics. By this time, though, at least half of his energy was devoted to finance. That same year, Sornette, Bouchaud, and Sornette’s postdoctoral researcher, Anders Johansen, realized that Sornette’s earlier work on predicting earthquakes and ruptures could be extended to predicting market crashes. They published a paper together in another physics journal. Amazingly, just a few months later, Sornette detected the log-periodic pattern that he had determined should presage a crash. The success of October 1997 deepened his belief that he was on to something important, and he redoubled his efforts on economics and financial modeling.

For this reason, assuming normal or log-normal distributions can lead to extremely misleading results and, moreover, produce a false sense of confidence regarding the likelihood of certain kinds of extreme events. For another, despite the fact that extreme events happen infrequently on both models, in Mandelbrot’s models of financial markets, they happen often enough that it is the extreme events that dominate market behavior in the long run. And so, even if there are similarities in how the models predict markets on a typical day, there is a significant difference in how one should view the importance of a “typical day” for the long-term behavior of markets. “It is also too simple to say that Mandelbrot was ignored . . .”: For instance, see Fama (1964). “Today, the best evidence indicates . . .”: See, for instance, Cont (2001) and references therein; this point was also emphasized in conversation by Didier Sornette, whose work is the subject of Chapter 7


The Smartest Investment Book You'll Ever Read: The Simple, Stress-Free Way to Reach Your Investment Goals by Daniel R. Solin

Alan Greenspan, asset allocation, buy and hold, corporate governance, diversification, diversified portfolio, index fund, John Bogle, market fundamentalism, money market fund, Myron Scholes, PalmPilot, passive investing, prediction markets, prudent man rule, random walk, risk tolerance, risk-adjusted returns, risk/return, transaction costs, Vanguard fund, zero-sum game

Su international stocks Palaro, Helder P. , 166 Palmer, RosseJl E., 73 PAS (Porrfolio Allocation Score), 178 Su also Asset Allocation Q uestionnaire passive mvestmenrs index funds and ETFs as, 8, 18 See also ETFs (exchange traded funds); index funds Patalon, William, 111, 62 pension funds, as Smarr InvestOrs, predictions, market. Se, market timing (predicting the future) principal-protected notes (PPNs),I64 Professional Financial Advisor, The(De Gocy) , 155-56, 181 prospectuS for a fund, 60 Prudent Investor Rule, 99-101 Prudential house funds , 77, 163 psychology of investing desire for order, 3 1 gambling, 30-3 1, 153-54 irrational exuberance in, 49-50, 157 market psychology, 49 marketing and, 30 overconfidence, 32 personal values and, 34-35 research on, 152-54 ~s jz.zle" and excitement, 32-33 stress of, 25 Su also Asset Allocation Questionnaire 105-6, 139-40, 168 pension plans (RRPs and RRSPs) ETFs in, 139-40, 169-70 Porrfolio Allocation Score (PAS), 178 Su also ruse t Allocation Questionnaire Portfolio ~kction (Markowitz), 67,162 poHfolios, inveSlment.


pages: 393 words: 115,217

Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries by Safi Bahcall

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, AOL-Time Warner, Apollo 11, Apollo 13, Apple II, Apple's 1984 Super Bowl advert, Astronomia nova, behavioural economics, Boeing 747, British Empire, Cass Sunstein, Charles Lindbergh, Clayton Christensen, cognitive bias, creative destruction, disruptive innovation, diversified portfolio, double helix, Douglas Engelbart, Douglas Engelbart, Dunbar number, Edmond Halley, Gary Taubes, Higgs boson, hypertext link, industrial research laboratory, invisible hand, Isaac Newton, Ivan Sutherland, Johannes Kepler, Jony Ive, knowledge economy, lone genius, Louis Pasteur, Mark Zuckerberg, Menlo Park, Mother of all demos, Murray Gell-Mann, PageRank, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, power law, prediction markets, pre–internet, Ralph Waldo Emerson, RAND corporation, random walk, reality distortion field, Richard Feynman, Richard Thaler, Sheryl Sandberg, side project, Silicon Valley, six sigma, stem cell, Steve Jobs, Steve Wozniak, synthetic biology, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tim Cook: Apple, tulip mania, Wall-E, wikimedia commons, yield management

We’ll extend these ideas on the behavior of groups to the behavior of societies and nations, and see how that helps us understand the course of history: why tiny Britain, for example, toppled the far larger and wealthier empires of India and China. This may all sound a bit … loony. That’s the idea. * * * To begin, we will turn to an engineer handed a national crisis. Let’s turn to the brink of world war. PART ONE ENGINEERS OF SERENDIPITY 1 How Loonshots Won a War Life on the edge Had there been prediction markets in 1939, the odds would have favored Nazi Germany. In the looming battle between world powers, the Allies lagged far behind Germany in what Winston Churchill described as the “secret war”: the race for more powerful technologies. Germany’s new submarines, called U-boats, threatened to dominate the Atlantic and strangle supply lines to Europe.

A mechanical elephant to carry military equipment through the jungles of Vietnam. A superbomb made from the element hafnium, discovered, allegedly, by a physicist experimenting with a dental x-ray machine. A plan for achieving nuclear fusion through rapidly collapsing bubbles inside liquids (a modified cleaning fluid was used). A prediction market where investors could bet on the location of the next terrorist event, in order to tap into the “wisdom of crowds.” (The project was scrapped for what might be called bad taste.) Other DARPA loonshots have transformed industries or created new academic disciplines. The early computer network ARPANET evolved into the internet.


pages: 149 words: 43,747

How I Invest My Money: Finance Experts Reveal How They Save, Spend, and Invest by Brian Portnoy, Joshua Brown

asset allocation, behavioural economics, bitcoin, blockchain, blue-collar work, buy and hold, coronavirus, COVID-19, cryptocurrency, diversification, diversified portfolio, estate planning, financial independence, fixed income, high net worth, housing crisis, index fund, John Bogle, low interest rates, mental accounting, passive investing, prediction markets, risk tolerance, Salesforce, Sharpe ratio, time value of money, underbanked, Vanguard fund

Sounds simple, but present-day investors continue to dissect far-reaching policy implications of COVID-19 government actions in 2020, which include fiscal policy and global monetary policy adding yet another round of stimulus to markets in an effort to rescue the US economy. Asset allocators are faced with recalibrating allocation models to incorporate increased volatility, uncertainty, complexity, and ambiguity. Volatility—currencies, global equities, and fixed income market volatility as well as the absence of stable and predictable markets and regulation. Uncertainty—wide swings in monetary and fiscal policy over the course of months or even weeks. Complexity—markets become riskier the larger that the ETF space becomes. The shift towards passive funds has the potential to concentrate investments in a few large products which increases systemic risk, making markets more susceptible to the flows of a few large passive products.


pages: 960 words: 125,049

Mastering Ethereum: Building Smart Contracts and DApps by Andreas M. Antonopoulos, Gavin Wood Ph. D.

air gap, Amazon Web Services, bitcoin, blockchain, business logic, continuous integration, cryptocurrency, Debian, digital divide, Dogecoin, domain-specific language, don't repeat yourself, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, functional programming, Google Chrome, information security, initial coin offering, intangible asset, Internet of things, litecoin, machine readable, move fast and break things, node package manager, non-fungible token, peer-to-peer, Ponzi scheme, prediction markets, pull request, QR code, Ruby on Rails, Satoshi Nakamoto, sealed-bid auction, sharing economy, side project, smart contracts, transaction costs, Turing complete, Turing machine, Vickrey auction, Vitalik Buterin, web application, WebSocket

., incorporating interest rates into smart financial derivatives Static/pseudostatic data: security identifiers, country codes, currency codes, etc. Time and interval data: for event triggers grounded in precise time measurements Weather data: e.g., insurance premium calculations based on weather forecasts Political events: for prediction market resolution Sporting events: for prediction market resolution and fantasy sports contracts Geolocation data: e.g., as used in supply chain tracking Damage verification: for insurance contracts Events occurring on other blockchains: interoperability functions Ether market price: e.g., for fiat gas price oracles Flight statistics: e.g., as used by groups and clubs for flight ticket pooling In the following sections, we will examine some of the ways oracles can be implemented, including basic oracle patterns, computation oracles, decentralized oracles, and oracle client implementations in Solidity.


pages: 161 words: 44,488

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar

Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business logic, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, decentralized internet, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, Ford Model T, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, Vitalik Buterin, web application, Yochai Benkler

We will own the data that belongs to us. We will self-manage our online reputations, and as we interact with various people or businesses, only the relevant slices of data will be revealed to them. Cryptocurrency-only banks will emerge, offering a variety of financial services based on virtual currencies. Decentralized prediction markets will enter the mainstream and offer frequent and credible predictions. Distributed Autonomous Organizations (DAOs) will become viable, with self-governed operations and user-generated value creation that tie-back directly to services and financial rewards. Spontaneous and trusted commerce will happen between peers, without central intermediaries, and with little to no friction.


How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Bear Stearns, Black-Scholes formula, Bob Litterman, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, business process, butter production in bangladesh, buy and hold, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency risk, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, full employment, George Akerlof, global macro, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, Ivan Sutherland, John Bogle, John von Neumann, junk bonds, linear programming, Loma Prieta earthquake, Long Term Capital Management, machine readable, margin call, market friction, market microstructure, martingale, merger arbitrage, Michael Milken, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Richard Feynman, Richard Stallman, risk free rate, risk-adjusted returns, risk/return, seminal paper, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, subscription business, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

By way of introduction, we will explain from our perspective the roots, roles, and contributions of the Wall Street quant. We begin by defining the Quant. Mark Joshi, a famous quant, has proposed this definition: A quant designs and implements mathematical models for the pricing of derivatives, assessment of risk, or predicting market movements.1 1 JWPR007-Lindsey 2 May 7, 2007 18:27 h ow i b e cam e a quant Perhaps some of the terms used in this definition require definition themselves. A mathematical model is a formula, equation, group of equations, or computational algorithm that attempts to explain some type of relationship.

A humble beginning, certainly, as only 911 option contracts were traded on 16 different equities. Now, each year, hundreds of millions of equity option contracts on thousands of companies trade on dozens of exchanges (both physical and electronic) around the world. The key ingredient that ties quants to derivatives and the other two functions identified by Joshi (risk assessment and predicting markets) is mathematical know-how. The Black-Scholes option pricing formula is a good example of this. JWPR007-Lindsey May 7, 2007 18:27 Introduction 3 The model, as it was first presented, was obtained by employing a result from physics, the solution to a particular partial differential equation called the heat-transfer equation.


pages: 457 words: 128,838

The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order by Paul Vigna, Michael J. Casey

Airbnb, Alan Greenspan, altcoin, Apple Newton, bank run, banking crisis, bitcoin, Bitcoin Ponzi scheme, blockchain, Bretton Woods, buy and hold, California gold rush, capital controls, carbon footprint, clean water, Cody Wilson, collaborative economy, collapse of Lehman Brothers, Columbine, Credit Default Swap, cross-border payments, cryptocurrency, David Graeber, decentralized internet, disinformation, disintermediation, Dogecoin, driverless car, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, Firefox, Flash crash, Ford Model T, Fractional reserve banking, Glass-Steagall Act, hacker house, Hacker News, Hernando de Soto, high net worth, informal economy, intangible asset, Internet of things, inventory management, Joi Ito, Julian Assange, Kickstarter, Kuwabatake Sanjuro: assassination market, litecoin, Long Term Capital Management, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, Money creation, money: store of value / unit of account / medium of exchange, Nelson Mandela, Network effects, new economy, new new economy, Nixon shock, Nixon triggered the end of the Bretton Woods system, off-the-grid, offshore financial centre, payday loans, Pearl River Delta, peer-to-peer, peer-to-peer lending, pets.com, Ponzi scheme, prediction markets, price stability, printed gun, profit motive, QR code, RAND corporation, regulatory arbitrage, rent-seeking, reserve currency, Robert Shiller, Ross Ulbricht, Satoshi Nakamoto, seigniorage, shareholder value, sharing economy, short selling, Silicon Valley, Silicon Valley startup, Skype, smart contracts, special drawing rights, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, supply-chain management, Ted Nelson, The Great Moderation, the market place, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, Turing complete, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, underbanked, Vitalik Buterin, WikiLeaks, Y Combinator, Y2K, zero-sum game, Zimmermann PGP

There are individual losses and socialized gains.” Asked to describe the job market if and when the kinds of decentralized autonomous companies envisaged by his firm become prevalent, BitShares CEO Daniel Larimer confidently predicts that these projects “can create millions of information-based jobs.” What’s more, he says, blockchain-based prediction markets, where people buy and sell contracts that pay out depending on how accurately they predict an event, will create new moneymaking opportunities in the intermediary industries destined for disruption. “If you’re a middleman in the lending industry or a middleman in commodities, or have medical knowledge, you know that industry better than anyone else, which means you can take the knowledge you have and turn it into value,” Larimer says.

Larimer’s jobs-for-everyone utopianism—the pervading ethos of Silicon Valley, shared by many bitcoiners—glosses over how many, if not most, people find change difficult. Not all, and perhaps not many, laid-off workers can easily pick themselves up and parlay their knowledge into making an income from speculative trading on a BitShares prediction market. To many it will seem like a form of gambling. To subject their lives to such uncertainty is anathema to people who’ve expected a salaried job to last a lifetime and to provide security and permanence. People will have to figure out how to apply their particular skills to this Brave New World and, if they can’t apply them, how to rapidly acquire the right skills.


pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett

Ada Lovelace, Airbnb, AlphaGo, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, blockchain, Boris Johnson, Californian Ideology, Cambridge Analytica, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, disinformation, Dominic Cummings, Donald Trump, driverless car, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, Filter Bubble, future of work, general purpose technology, gig economy, global village, Google bus, Hans Moravec, hive mind, Howard Rheingold, information retrieval, initial coin offering, Internet of things, Jeff Bezos, Jeremy Corbyn, job automation, John Gilmore, John Maynard Keynes: technological unemployment, John Perry Barlow, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta-analysis, mittelstand, move fast and break things, Network effects, Nicholas Carr, Nick Bostrom, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, post-truth, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Bannon, Steve Jobs, Steven Levy, strong AI, surveillance capitalism, TaskRabbit, tech worker, technological singularity, technoutopianism, Ted Kaczynski, TED Talk, the long tail, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator, you are the product

The whole scene is bursting with zeal, energy and billions of dollars. All sorts of blockchain applications have recently been released: OpenBazaar, a peer-to-peer marketplace that is impossible to shut down, decentralised file storage, a distributed web domain name system, land ownership records in India to combat fraud and prediction markets. Several are working on social media applications that are impossible to censor or control because they’re hosted on a decentralised blockchain. Perhaps the most important functionality of the new wave of blockchains is the way they allow ‘smart contracts’, lines of code that execute instructions automatically.


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Black Swan, Bob Litterman, bond market vigilante , book value, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, carbon credits, Carmen Reinhart, central bank independence, classic study, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, deal flow, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global macro, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, inverted yield curve, invisible hand, John Bogle, junk bonds, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, pension time bomb, performance metric, Phillips curve, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, savings glut, search costs, selection bias, seminal paper, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stock buybacks, stocks for the long run, survivorship bias, systematic trading, tail risk, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

These data start only in 1985, but academic studies document good market-timing ability using total payout yield and net buyback yield since the 1920s. The strong results may partly reflect corporate managers successfully market timing their equity issuance to coincide with expensive market levels. Table 8.3. Predictive market-timing correlations of narrow and broad carry measures, 1985–2009 Sources: Haver Analytics, Michael Afreh (Nomura). 8.4.2 Value measures: Earnings yield and the Fed Model Absolute valuation A stock market’s price–earnings (P/E) ratio and its inverse, the earnings yield (E/P), are the most popular equity market valuation indicators.

Disappointments following excessive growth optimism may be a key reason for the success of contrarian stock selection and market-timing strategies. The share of prime-age savers in the population peaked in Japan in the late 1980s and in the U.S. around 2000, consistent with broad patterns in market valuations. Some observers call the 1990s “the baby-boomer rally” in the U.S. and predict market declines when the retiring boomers begin to dissave (i.e., use past savings for current consumption). The story rings true but demographic developments are at best only one influence on asset prices. Moreover, there is some evidence of retirees continuing to save rather than dissave and the story ignores the entry of savers from younger emerging market countries. 8.4.3 Ex ante equity premia based on the DDM While the yield ratio is useful shorthand for the equity–bond premium, the dividend discount model (DDM) gives us directly what we want to see: a numerical estimate of the difference between stocks’ and bonds’ expected long-run returns.

This average correlation turns out to have a significantly positive relation to next quarter equity market returns, while average variance has virtually no relation (and indeed a negative one once the correlation’s predictive ability is controlled for). Apparently, the average variance component offsets and conceals the market-timing ability of correlation, leaving stock market variance unable to predict market returns. Table 19.1. Correlations between monthly S&P 500 returns and contemporaneous and lagged realized one-month volatility levels (Vol) and changes (dVol), 1928–2009 (and VIX 1986–2009) Sources: Bloomberg. Bollerslev–Zhou (2007) present the most positive evidence for a conditional risk–reward relation so far.


pages: 200 words: 54,897

Flash Boys: Not So Fast: An Insider's Perspective on High-Frequency Trading by Peter Kovac

bank run, barriers to entry, bash_history, Bernie Madoff, compensation consultant, computerized markets, computerized trading, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, payment for order flow, prediction markets, price discovery process, proprietary trading, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day

The value of its ability to buy Microsoft from you at $30 a share and to hold the shares for a few microseconds – knowing that, even if the Microsoft share price began to fall, it could turn around and sell the shares at $30.01 – was determined by how likely it was that Microsoft’s share price, in those magical microseconds, would rise in price.” Lewis faces a dilemma: how can you argue that volatility – the unpredictability of a stock’s price – somehow benefits high-frequency traders, whom he has been arguing make all their money by predicting market moves? It seems that unpredictability would ruin whatever scam they have going. It’s a very difficult argument to make, even more so given the principle of “adverse selection.” Adverse selection is the fancy economic term that means that a market-maker who sticks his neck out is the first one to get stuck with a losing position when the market drops.


pages: 188 words: 9,226

Collaborative Futures by Mike Linksvayer, Michael Mandiberg, Mushon Zer-Aviv

4chan, AGPL, Benjamin Mako Hill, British Empire, citizen journalism, cloud computing, collaborative economy, corporate governance, crowdsourcing, Debian, Eben Moglen, en.wikipedia.org, fake news, Firefox, informal economy, jimmy wales, Kickstarter, late capitalism, lolcat, loose coupling, Marshall McLuhan, means of production, Naomi Klein, Network effects, optical character recognition, packet switching, planned obsolescence, postnationalism / post nation state, prediction markets, Richard Stallman, semantic web, Silicon Valley, slashdot, Slavoj Žižek, stealth mode startup, technoutopianism, The future is already here, the medium is the message, The Wisdom of Crowds, web application, WikiLeaks, Yochai Benkler

Open Innovation refers to a collection of tools and methods for enabling more collaboration. Some of these Open Innovation tools include crowdsourcing of research expertise which is being lead by a company called InnoCentive, patent pools, end-user innovation which Erik von Hippel documented in Democratizing Innovation, and wisdom of the crowds methods such as prediction markets. 117 Reputation is an important question for many forms of collaboration, but particularly in science, where careers are determined primarily by one narrow metric of reputation—publication. If the above phenomena are to reach their full potential, they will have to be aligned with scientific career incentives.


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Alvin Toffler, Atul Gawande, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, Carl Icahn, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, Future Shock, game design, global macro, hindsight bias, housing crisis, index fund, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, managed futures, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, Market Wizards by Jack D. Schwager, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, Teledyne, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

Technical traders believe that a careful analysis of daily price action is an effective means of trading for profit. Now here is where an understanding of technical analysis becomes complicated. There are essentially two forms of technical analysis. One form is based on an ability to “read” charts and use “indicators” to predict market direction. Here is an example of the mentality behind a predictive view of technical analysis: 9 But I think our ace in the hole is that the governments usually screw things up and don’t maintain their sound money and policy coordination. And about the time we’re ready to give up on what usually has worked, and proclaim that the world has now changed, the governments help us out by creating unwise policy that helps produce dislocations and trends.

The second chart is the British pound (Chart 2.4) where, unlike the Japanese yen, the market proved unfavorable for Dunn. It was a typical whipsaw market, which is always difficult for trend followers because small losses add up. You can see how he entered and was stopped out; then entered and was stopped out again. Remember, trend followers don’t predict markets, they react to them—so the small losses were part of the game. He managed the small losses because the British pound was only a portion of his 37 Chapter 2 • Great Trend Followers portfolio. His yen trade more than made up for his losses on the British pound trade, because no matter how uncomfortable others are with his approach, for Dunn, big winners offset small losers in the long run.


pages: 542 words: 145,022

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest by Andrew W. Lo, Stephen R. Foerster

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, backtesting, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, Charles Babbage, Charles Lindbergh, compound rate of return, corporate governance, COVID-19, credit crunch, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, Edward Glaeser, equity premium, equity risk premium, estate planning, Eugene Fama: efficient market hypothesis, fake news, family office, fear index, fiat currency, financial engineering, financial innovation, financial intermediation, fixed income, hiring and firing, Hyman Minsky, implied volatility, index fund, interest rate swap, Internet Archive, invention of the wheel, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John von Neumann, joint-stock company, junk bonds, Kenneth Arrow, linear programming, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, managed futures, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, money market fund, money: store of value / unit of account / medium of exchange, Myron Scholes, new economy, New Journalism, Own Your Own Home, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, prediction markets, price stability, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, selection bias, seminal paper, shareholder value, Sharpe ratio, short selling, South Sea Bubble, stochastic process, stocks for the long run, survivorship bias, tail risk, Thales and the olive presses, Thales of Miletus, The Myth of the Rational Market, The Wisdom of Crowds, Thomas Bayes, time value of money, transaction costs, transfer pricing, tulip mania, Vanguard fund, yield curve, zero-coupon bond, zero-sum game

The empirical work has also changed the views and practices of market professionals.”35 The debate about market efficiency continues to play out most vigorously in the arena of investment management, pitting active managers who try to beat the market versus passive managers who try to mimic the market. As Fama recently said, “There’s quite a bit of evidence that even professionals don’t show any ability to pick stocks or to predict market rollbacks. Most of the people we identify as skilled based on returns have probably just been lucky.”36 It may be better to be lucky than smart, but luck in the future isn’t guaranteed. The notion of market efficiency has expanded well beyond the stock market, even into the basketball arena.

While the option market, and the beauty of the Black-Scholes technology and Merton follow-on is, essentially, it decomposes and tells you what the risk is.”68 In other words, stock prices can go up or down because either the stock’s anticipated cash flow growth changes or its perceived risk changes. However, the change in the price of an option is unambiguously tied to changes in risk, and it’s that assessment of risk that is quantified by the market. Scholes gave the example of election prediction markets, such as the University of Iowa’s Iowa Electronic Markets, in which futures market contract payoffs are based on political outcomes. “Who’s going to win the election? We have election markets. People say, ‘How can a market know anything about elections?’ It comes up once every four years, or so.… The market is amazing, how accurate it is, relative to the pundits.”69 Derivatives as Financial Weapons of Mass Destruction?


pages: 506 words: 151,753

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze by Laura Shin

"World Economic Forum" Davos, 4chan, Airbnb, altcoin, bike sharing, bitcoin, blockchain, Burning Man, cloud computing, complexity theory, Credit Default Swap, cryptocurrency, DevOps, digital nomad, distributed ledger, Dogecoin, Donald Trump, Dutch auction, Edward Snowden, emotional labour, en.wikipedia.org, Ethereum, ethereum blockchain, fake news, family office, fiat currency, financial independence, Firefox, general-purpose programming language, gravity well, hacker house, Hacker News, holacracy, independent contractor, initial coin offering, Internet of things, invisible hand, Johann Wolfgang von Goethe, Julian Assange, Kickstarter, litecoin, low interest rates, Mark Zuckerberg, minimum viable product, off-the-grid, performance metric, Potemkin village, prediction markets, QR code, ride hailing / ride sharing, risk tolerance, risk/return, Satoshi Nakamoto, sharing economy, side project, Silicon Valley, Skype, smart contracts, social distancing, software as a service, Steve Jobs, Turing complete, Vitalik Buterin, Wayback Machine, WikiLeaks

Four days later, while she was celebrating her birthday on a boat near Catalina Island and he was in Los Angeles, they tried domain names on GoDaddy, but some were really expensive, like $200. MyEtherWallet was $11.99. They nabbed it.7 RIGHT WHEN THEY launched, the presale for Augur, a decentralized prediction market in which people could make predictions and bet on the outcome, was happening. When Taylor went to put money in, she was stymied, again, by challenging technical instructions. She asked Kosala to make a one-click button for her. He did, and they added an “Augur Crowdsale” tab to the site.8 Late in the sale, which ended October 1, 2015, the Augur newsletter gave a shout-out to MyEtherWallet for the button.

After raising $11.7 million in December, the haul swelled in January and February to around $67 million and $73 million, respectively. After a March dip to $22 million, in April, thirteen ICOs raked in almost $86 million. One ICO that blew the lid off even Golem’s in November was Gnosis (GNO), which, like Augur, was a decentralized prediction market. The Berlin-based team held the sale out of Gibraltar, a jurisdiction that, like Switzerland and Singapore, had crypto-friendly regulations. Their goal was $12.5 million, and they tried a new mechanism. Instead of a first-come-first-served-style ICO, they held a Dutch auction, which meant the starting price, $30, would be the ceiling.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

The same could happen if fixes are found for some of the bureaucratic deformations that warp organizational life—wasteful status games, mission creep, concealment or falsification of information, and other agency problems. Even partial solutions to these problems could pay hefty dividends for collective intelligence. The technological and institutional innovations that could contribute to the growth of our collective intelligence are many and various. For example, subsidized prediction markets might foster truth-seeking norms and improve forecasting on contentious scientific and social issues.78 Lie detectors (should it prove feasible to make ones that are reliable and easy to use) could reduce the scope for deception in human affairs.79 Self-deception detectors might be even more powerful.80 Even without newfangled brain technologies, some forms of deception might become harder to practice thanks to increased availability of many kinds of data, including reputations and track records, or the promulgation of strong epistemic norms and rationality culture.

New York: Dutton. Benyamin, B., Pourcain, B. St., Davis, O. S., Davies, G., Hansell, M. K., Brion, M.-J. A., Kirkpatrick, R. M., et al. 2013. “Childhood Intelligence is Heritable, Highly Polygenic and Associated With FNBP1L.” Molecular Psychiatry (January 23). Berg, Joyce E., and Rietz, Thomas A. 2003. “Prediction Markets as Decision Support Systems.” Information Systems Frontiers 5 (1): 79–93. Berger, Theodore W., Chapin, J. K., Gerhardt, G. A., Soussou, W. V., Taylor, D. M., and Tresco, P. A., eds. 2008. Brain–Computer Interfaces: An International Assessment of Research and Development Trends. Springer. Berger, T.


Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

A remarkable experiment by Langer and Roth showed that students who had success predicting a coin toss early on tended to believe they had superior coin toss prediction skills to those that did not. If people can convince themselves they are superior forecasters of a coin toss, imagine how they might convince themselves of their ability to predict markets! Langer and Roth reported their findings in “Heads I win, tails it’s chance”60. I love the title because it captures the faulty thinking of many an overconfident trader. If I make money on the trade, I’m smart. If I don’t, it was just bad luck. That’s not rational thinking! Example 1: You are playing poker.

There are entire books that go through all the major technical patterns and backtest them and come to the same result: technical analysis is not a good forecasting tool. I explained my philosophy on technical analysis when I discussed overreliance on simple indicators and apophenia. I want to make it clear though: I use technical analysis in my trading every day. I just don’t use it to predict markets. Technical analysis gives you an important set of tactical execution and risk management tools. It does not help you forecast market direction. Good traders are quick to admit when they are wrong. They make leveraged, asymmetric bets. Technical analysis can help you do these things better and provide strong and clear signals of when it’s time to give up on a bad idea or take profit on a good one.


Economic Gangsters: Corruption, Violence, and the Poverty of Nations by Raymond Fisman, Edward Miguel

accounting loophole / creative accounting, Andrei Shleifer, Asian financial crisis, barriers to entry, behavioural economics, blood diamond, clean water, colonial rule, congestion charging, crossover SUV, Donald Davies, European colonialism, failed state, feminist movement, George Akerlof, Great Leap Forward, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), invisible hand, mass immigration, megacity, oil rush, prediction markets, random walk, Scramble for Africa, selection bias, Silicon Valley, South China Sea, unemployed young men

The value of futures of the Standard and Poors 500 Index— a measure of what investors expected to happen to stock values the next morning—jumped by over 1 percent at around 2 a.m. on election night in 2000 when Bush was declared the winner in Florida, only to drop back down a couple of hours later when the Florida outcome was rescinded. Refer to “Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections,” by Erik Snowberg, Justin Wolfers, and Eric Zitzewitz in the Quarterly Journal of Economics (2007) for details. 5. “Health matters for Suharto’s children,” Financial Times, January 3, 1998. 6. A question that we often get when discussing these results is whether we personally profited in some way from our interest in 217 N O TES the stock market reaction to Suharto’s health.


The Techno-Human Condition by Braden R. Allenby, Daniel R. Sarewitz

"World Economic Forum" Davos, Abraham Maslow, airport security, Anthropocene, augmented reality, carbon credits, carbon footprint, clean water, cognitive dissonance, cognitive load, coherent worldview, conceptual framework, creative destruction, Credit Default Swap, decarbonisation, different worldview, Edward Jenner, facts on the ground, friendly fire, Hans Moravec, industrial cluster, information security, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, Jane Jacobs, land tenure, Lewis Mumford, life extension, Long Term Capital Management, market fundamentalism, mutually assured destruction, Nick Bostrom, nuclear winter, Peter Singer: altruism, planetary scale, precautionary principle, prediction markets, radical life extension, Ralph Waldo Emerson, Ray Kurzweil, Silicon Valley, smart grid, source of truth, stem cell, Stewart Brand, synthetic biology, technoutopianism, the built environment, The Wealth of Nations by Adam Smith, transcontinental railway, We are as Gods, Whole Earth Catalog

We doubt it, yet the larger point is this: Even if it seems as if we are simply modifying the constitution of humanness at the individual level, the systems-level effects of tens of millions of such modifications may plausibly begin to manifest in system-wide changes in human values and behaviors that cannot possibly be predicted. Markets are an information-processing mechanism built on, and assuming, stable legal, cultural, and institutional foundations, and a stable idea of what humans are; enhancement renders those foundations contingent and unpredictably changing. Markets assume a particular context; enhancement transforms it.


pages: 218 words: 68,648

Confessions of a Crypto Millionaire: My Unlikely Escape From Corporate America by Dan Conway

Affordable Care Act / Obamacare, Airbnb, bank run, basic income, Bear Stearns, Big Tech, bitcoin, blockchain, buy and hold, cloud computing, cognitive dissonance, corporate governance, crowdsourcing, cryptocurrency, disruptive innovation, distributed ledger, double entry bookkeeping, Ethereum, ethereum blockchain, fault tolerance, financial independence, gig economy, Gordon Gekko, Haight Ashbury, high net worth, holacracy, imposter syndrome, independent contractor, initial coin offering, job satisfaction, litecoin, Marc Andreessen, Mitch Kapor, obamacare, offshore financial centre, Ponzi scheme, prediction markets, rent control, reserve currency, Ronald Coase, Satoshi Nakamoto, Silicon Valley, Silicon Valley billionaire, smart contracts, Steve Jobs, supercomputer in your pocket, tech billionaire, tech bro, Tragedy of the Commons, Turing complete, Uber for X, universal basic income, upwardly mobile, Vitalik Buterin

I wrote a Medium blog (“Enter the Ethereum Economy”) that breathlessly explained my charge: For those of us who believe in decentralization and have committed to the cause as a funder of a particular token, as a career move, or as an evangelist — this is a gathering place to figure out how we can help, compare notes, talk strategy, hear interesting speakers and network. We are going to need to pull on one another as this thing heats up and the real ride begins. Two hundred people signed up. My first meeting featured a presentation by Martin Koppelman of the prediction market dApp Gnosis, one of the hottest projects at the time. Six months later, it would be valued at an eye-popping one billion after its Initial Coin Offering. I invited my sister Kathleen and a couple of friends who were tech-savvy, mildly interested, and willing to do me a solid. Martin’s presentation was thorough.


pages: 226 words: 65,516

Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street by Jeff John Roberts

4chan, Airbnb, Alan Greenspan, altcoin, Apple II, Bernie Sanders, Bertram Gilfoyle, Big Tech, bitcoin, blockchain, Blythe Masters, Bonfire of the Vanities, Burning Man, buttonwood tree, cloud computing, coronavirus, COVID-19, creative destruction, Credit Default Swap, cryptocurrency, democratizing finance, Dogecoin, Donald Trump, double helix, driverless car, Elliott wave, Elon Musk, Ethereum, ethereum blockchain, family office, financial engineering, Flash crash, forensic accounting, hacker house, Hacker News, hockey-stick growth, index fund, information security, initial coin offering, Jeff Bezos, John Gilmore, Joseph Schumpeter, litecoin, Marc Andreessen, Mark Zuckerberg, Masayoshi Son, Menlo Park, move fast and break things, Multics, Network effects, offshore financial centre, open borders, Paul Graham, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, radical decentralization, ransomware, regulatory arbitrage, reserve currency, ride hailing / ride sharing, Robert Shiller, rolodex, Ross Ulbricht, Sam Altman, Sand Hill Road, Satoshi Nakamoto, sharing economy, side hustle, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart contracts, SoftBank, software is eating the world, Startup school, Steve Ballmer, Steve Jobs, Steve Wozniak, transaction costs, Vitalik Buterin, WeWork, work culture , Y Combinator, zero-sum game

Instead of working to support new coins, Coinbase engineers were fiddling with ways to repackage existing offerings—creating bundles and index funds that offered the same boring coins. In another corner of the office, engineers were at work building something called Toshi. This was a tool to navigate dApps—short for decentralized applications—which Brian and others believed would be the future of crypto. A dApp can be anything from a word-processing tool to a prediction market, but what makes dApps distinct is the lack of a central company or manager. Imagine taking the sort of software programs you find in Microsoft Office and then running them on a bitcoin-style network. Unlike what you find in the app stores of Apple and Google, dApps can be distributed without anyone’s permission and rely on random computers around the world to operate. dApps are not the most efficient form of software, not least because you need a special browser to access them in the first place, but their supporters say they represent the next generation of computing.


pages: 210 words: 65,833

This Is Not Normal: The Collapse of Liberal Britain by William Davies

Airbnb, basic income, Bernie Sanders, Big bang: deregulation of the City of London, Black Lives Matter, Boris Johnson, Cambridge Analytica, central bank independence, centre right, Chelsea Manning, coronavirus, corporate governance, COVID-19, credit crunch, data science, deindustrialization, disinformation, Dominic Cummings, Donald Trump, double entry bookkeeping, Edward Snowden, fake news, family office, Filter Bubble, Francis Fukuyama: the end of history, ghettoisation, gig economy, global pandemic, global village, illegal immigration, Internet of things, Jeremy Corbyn, late capitalism, Leo Hollis, liberal capitalism, loadsamoney, London Interbank Offered Rate, mass immigration, moral hazard, Neil Kinnock, Northern Rock, old-boy network, post-truth, postnationalism / post nation state, precariat, prediction markets, quantitative easing, recommendation engine, Robert Mercer, Ronald Reagan, sentiment analysis, sharing economy, Silicon Valley, Slavoj Žižek, statistical model, Steve Bannon, Steven Pinker, surveillance capitalism, technoutopianism, The Chicago School, Thorstein Veblen, transaction costs, universal basic income, W. E. B. Du Bois, web of trust, WikiLeaks, Yochai Benkler

This is why it was so absurd to look to currency markets and spread-betters for the truth of what would happen in the referendum: they could only give a sense of what certain people felt would happen in the referendum at certain times. Given the absence of any trustworthy facts (in the form of polls), they could then only provide a sense of how investors felt about Britain’s national mood: a sentiment regarding a sentiment. As the 23 June 2016 turned into 24 June, it became manifestly clear that prediction markets are little more than an aggregative representation of the same feelings and moods that one might otherwise detect via Twitter. They’re not in the business of truth-telling, but of mood-tracking. What Sort of Crisis Is This? The bankruptcy of Lehman Brothers in September 2008 was an emergency.


pages: 233 words: 75,712

In Defense of Global Capitalism by Johan Norberg

anti-globalists, Asian financial crisis, capital controls, clean water, correlation does not imply causation, creative destruction, Deng Xiaoping, Edward Glaeser, export processing zone, Gini coefficient, Great Leap Forward, half of the world's population has never made a phone call, Hernando de Soto, illegal immigration, income inequality, income per capita, informal economy, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Joseph Schumpeter, Kenneth Rogoff, land reform, Lao Tzu, liberal capitalism, market fundamentalism, Mexican peso crisis / tequila crisis, Naomi Klein, new economy, open economy, prediction markets, profit motive, race to the bottom, rising living standards, Silicon Valley, Simon Kuznets, structural adjustment programs, The Wealth of Nations by Adam Smith, Tobin tax, trade liberalization, trade route, transaction costs, trickle-down economics, Tyler Cowen, union organizing, zero-sum game

Suppose a company extracts a metal and the price of that metal suddenly falls dramatically; earnings fail to materialize and bankruptcy threatens. Instead of devoting a large part of its activity to wondering how markets are going to develop, the firm can buy a right to sell the raw material at a predetermined price later on—a sale option. The purchaser of this option takes over the risk and the responsibility of predicting market developments. The metal company can quietly concentrate on extracting the metal, and the risk is willingly taken over instead by people who specialize in observing developments and apportioning the risks—in a word, speculators. Because exchange rates are rapidly changeable, firms encounter the same risk if, for example, the currency in which they are paid quickly depreciates.


The Handbook of Personal Wealth Management by Reuvid, Jonathan.

asset allocation, banking crisis, BRICs, business cycle, buy and hold, carbon credits, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, currency risk, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, global macro, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, low interest rates, managed futures, market bubble, merger arbitrage, negative equity, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, proprietary trading, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

Contemporary art accounts for much of the current interest in this field, owing to greater liquidity, frequent trades and a growing belief in its long-term value. Developing these activities, some have proposed an art exchange as a mechanism for the issuing of securities with art or collections as the underlying asset. So far, it is possible to trade art in the prediction market via the website Intrade. A related development has been the continued growth in rental income from art. In 1972, the Canadian government was the first to set up an art rental project aimed at government agencies. It allocated $5m over five years. With some 18,000 paintings, prints, photographs and sculptures by over 2,500 artists, the Canada Council Art Bank is now home to the largest collection of contemporary Canadian art and is the most important player in the Canadian art market.


pages: 249 words: 77,342

The Behavioral Investor by Daniel Crosby

affirmative action, Asian financial crisis, asset allocation, availability heuristic, backtesting, bank run, behavioural economics, Black Monday: stock market crash in 1987, Black Swan, book value, buy and hold, cognitive dissonance, colonial rule, compound rate of return, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, disinformation, diversification, diversified portfolio, Donald Trump, Dunning–Kruger effect, endowment effect, equity risk premium, fake news, feminist movement, Flash crash, haute cuisine, hedonic treadmill, housing crisis, IKEA effect, impact investing, impulse control, index fund, Isaac Newton, Japanese asset price bubble, job automation, longitudinal study, loss aversion, market bubble, market fundamentalism, mental accounting, meta-analysis, Milgram experiment, moral panic, Murray Gell-Mann, Nate Silver, neurotypical, Nick Bostrom, passive investing, pattern recognition, Pepsi Challenge, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Richard Feynman, Richard Thaler, risk tolerance, Robert Shiller, science of happiness, Shai Danziger, short selling, South Sea Bubble, Stanford prison experiment, Stephen Hawking, Steve Jobs, stocks for the long run, sunk-cost fallacy, systems thinking, TED Talk, Thales of Miletus, The Signal and the Noise by Nate Silver, Tragedy of the Commons, trolley problem, tulip mania, Vanguard fund, When a measure becomes a target

After the participants had spent some time trading and learning the market they switched conditions, with those in the growth market entering the volatile market, and vice versa. What they observed next was fascinating and surprising: people used different parts of their brain to make future investment decisions based on their early experience with the market. Those in Group One who had started with an orderly, predictable market organized their brain activity to create rules and search for universally applicable principles of the market. In the words of the researchers, “decision making would be supported by comparison of predicted and actual prices, and it would be driven by a rule based reasoning.” Conversely, those who began in the more chaotic condition utilized entirely separate parts of the brain to cope with the volatility of their market.


pages: 300 words: 77,787

Investing Demystified: How to Invest Without Speculation and Sleepless Nights by Lars Kroijer

Andrei Shleifer, asset allocation, asset-backed security, Bernie Madoff, bitcoin, Black Swan, BRICs, Carmen Reinhart, clean tech, compound rate of return, credit crunch, currency risk, diversification, diversified portfolio, equity premium, equity risk premium, estate planning, fixed income, high net worth, implied volatility, index fund, intangible asset, invisible hand, John Bogle, Kenneth Rogoff, low interest rates, market bubble, money market fund, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, selection bias, sovereign wealth fund, too big to fail, transaction costs, Vanguard fund, yield curve, zero-coupon bond

The markets will dominate the headlines and be a topic of conversation at work, the gym, meals out, in homes and everywhere else. How can you not have a view? The point is that we still don’t have a view. While many people with the benefit of hindsight say they saw the rebound, ‘just’ because there is great market turbulence does not mean that an investor is better able to predict market movements. We don’t consider ourselves smarter than the average dollar invested in the market, and that average dollar put the S&P 500 at an index value of under 700 in March 2009. The fact that four years later we see that same index trading around its all-time highs does not mean that we could predict in March 2009 that this would be the case.


pages: 260 words: 76,223

Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It. by Mitch Joel

3D printing, Amazon Web Services, augmented reality, behavioural economics, call centre, clockwatching, cloud computing, content marketing, digital nomad, do what you love, Firefox, future of work, gamification, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, QR code, recommendation engine, Richard Florida, risk tolerance, Salesforce, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, TechCrunch disrupt, TED Talk, the long tail, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, vertical integration, white picket fence, WikiLeaks, zero-sum game

The only real question is this: If a multimillion-dollar business can be developed and managed by one person with a laptop in an apartment, what happens to your business and your job as this rapid innovation and digitization continues to ripple through every industry? There is a startling realization in Arment’s story: There’s a good chance that your current business (or job) may not even be around in the next five years. Wacky prediction? Marketing hype to sell a book? It isn’t. Arment’s story isn’t an anomaly. Did you ever think it was even possible to have a one-person company with well over one million customers and a multimillion-dollar valuation? Welcome to purgatory (or heaven, if you’re Marco Arment). Apple co-founder and former CEO Steve Jobs once said, “If you don’t cannibalize yourself, someone else will.”


pages: 309 words: 78,361

Plenitude: The New Economics of True Wealth by Juliet B. Schor

Asian financial crisis, behavioural economics, big-box store, business climate, business cycle, carbon footprint, carbon tax, clean tech, Community Supported Agriculture, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, decarbonisation, degrowth, dematerialisation, demographic transition, deskilling, Edward Glaeser, en.wikipedia.org, Gini coefficient, global village, Herman Kahn, IKEA effect, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jevons paradox, Joseph Schumpeter, Kenneth Arrow, knowledge economy, life extension, McMansion, new economy, ocean acidification, off-the-grid, peak oil, pink-collar, post-industrial society, prediction markets, purchasing power parity, radical decentralization, ride hailing / ride sharing, Robert Shiller, sharing economy, Simon Kuznets, single-payer health, smart grid, systematic bias, systems thinking, The Chicago School, Thomas L Friedman, Thomas Malthus, too big to fail, transaction costs, Yochai Benkler, Zipcar

Each year, the U.S. government underwrites the oil, gas, nuclear, and coal industries to the tune of tens (or, depending on one’s definition, hundreds) of billions, allows them to emit carbon without financial liability, and supports a massive automobile infrastructure and chemical-intensive farming. Finally, the assumption of an all-knowing market requires almost superhuman informational and processing demands. Investors have to understand the scientific evidence, combine it with economic, political, social, psychological, and demographic data, and predict market movements. Relying on market prices as indicators of the state of the natural world is risky, if not reckless. Trade-off Economics: The Unbearable Costliness of Nature Accounts that emphasize the ability of the market to develop alternatives for nature or to clean up the messes caused by growth tend to be thinking in the medium or long term.


The Jobs to Be Done Playbook: Align Your Markets, Organization, and Strategy Around Customer Needs by Jim Kalbach

Airbnb, Atul Gawande, Build a better mousetrap, Checklist Manifesto, Clayton Christensen, commoditize, data science, Dean Kamen, fail fast, Google Glasses, job automation, Kanban, Kickstarter, knowledge worker, Lean Startup, market design, minimum viable product, prediction markets, Quicken Loans, Salesforce, shareholder value, Skype, software as a service, Steve Jobs, subscription business, Zipcar

This chapter offers one of the best explanations of tying research insights to product architecture. It’s a bottom-up process: you can cluster observations multiple times to derive high-level categories. Young also discussed how to label parts of the resulting architectural model. PLAY Test Hypotheses with JTBD Innovation brings uncertainty. No inventor can predict market success. Introducing new offerings to the market is risky because the consumers ultimately decide to adopt an innovation or not, regardless of the inherent benefits of the solution. Just consider the launch of the Segway, the famous self-propelled one-person scooter. The invention itself worked great and was very compelling to investors.


pages: 275 words: 84,980

Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives) by David Birch

"World Economic Forum" Davos, agricultural Revolution, Airbnb, Alan Greenspan, bank run, banks create money, bitcoin, blockchain, Bretton Woods, British Empire, Broken windows theory, Burning Man, business cycle, capital controls, cashless society, Clayton Christensen, clockwork universe, creative destruction, credit crunch, cross-border payments, cross-subsidies, crowdsourcing, cryptocurrency, David Graeber, dematerialisation, Diane Coyle, disruptive innovation, distributed ledger, Dogecoin, double entry bookkeeping, Ethereum, ethereum blockchain, facts on the ground, fake news, fault tolerance, fiat currency, financial exclusion, financial innovation, financial intermediation, floating exchange rates, Fractional reserve banking, index card, informal economy, Internet of things, invention of the printing press, invention of the telegraph, invention of the telephone, invisible hand, Irish bank strikes, Isaac Newton, Jane Jacobs, Kenneth Rogoff, knowledge economy, Kuwabatake Sanjuro: assassination market, land bank, large denomination, low interest rates, M-Pesa, market clearing, market fundamentalism, Marshall McLuhan, Martin Wolf, mobile money, Money creation, money: store of value / unit of account / medium of exchange, new economy, Northern Rock, Pingit, prediction markets, price stability, QR code, quantitative easing, railway mania, Ralph Waldo Emerson, Real Time Gross Settlement, reserve currency, Satoshi Nakamoto, seigniorage, Silicon Valley, smart contracts, social graph, special drawing rights, Suez canal 1869, technoutopianism, The future is already here, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, wage slave, Washington Consensus, wikimedia commons

For my retirement plan I need to find something unique to the virtual, which is why I was sufficiently interested in the novel concept of assassination markets to write about them. I was excited to find that an enterprising chap by the name of Kuwabatake Sanjuro had taken advantage of the invention of Bitcoin to set one up (Greenberg 2013). In case you are wondering what an assassination market is, it is a prediction market where any party can place a bet (using anonymous electronic money, and pseudonymous remailers) on the date of death of a given individual and collect a payoff if they ‘guess’ the date accurately. This would incentivize the assassination of specific individuals because the assassin, knowing when the action would take place, could profit by making an accurate bet on the time of the subject’s death.


pages: 322 words: 84,752

Pax Technica: How the Internet of Things May Set Us Free or Lock Us Up by Philip N. Howard

Aaron Swartz, Affordable Care Act / Obamacare, Berlin Wall, bitcoin, blood diamond, Bretton Woods, Brian Krebs, British Empire, butter production in bangladesh, call centre, Chelsea Manning, citizen journalism, Citizen Lab, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, digital map, Edward Snowden, en.wikipedia.org, Evgeny Morozov, failed state, Fall of the Berlin Wall, feminist movement, Filter Bubble, Firefox, Francis Fukuyama: the end of history, Google Earth, Hacker News, Howard Rheingold, income inequality, informal economy, information security, Internet of things, John Perry Barlow, Julian Assange, Kibera, Kickstarter, land reform, M-Pesa, Marshall McLuhan, megacity, Mikhail Gorbachev, mobile money, Mohammed Bouazizi, national security letter, Nelson Mandela, Network effects, obamacare, Occupy movement, off-the-grid, packet switching, pension reform, prediction markets, sentiment analysis, Silicon Valley, Skype, spectrum auction, statistical model, Stuxnet, Tactical Technology Collective, technological determinism, trade route, Twitter Arab Spring, undersea cable, uranium enrichment, WikiLeaks, zero day

It won’t matter whether China’s government adopts an open-data policy any time soon—the internet of things will generate enough data to keep many China watchers busy. People are creatively playing with data, some of it gleaned from reluctant governments. Some of that data feeds interesting predictive markets. Such markets don’t always work well, and because they trade in odds, you can count the number of times they don’t work at all. The models are getting better, and specialists use them to gauge popular and expert opinion on likely political outcomes. When will a dictator fall from power? Will Russia claim more territory from neighboring countries?


When Free Markets Fail: Saving the Market When It Can't Save Itself (Wiley Corporate F&A) by Scott McCleskey

Alan Greenspan, Asian financial crisis, asset-backed security, bank run, barriers to entry, Bear Stearns, Bernie Madoff, break the buck, call centre, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, financial engineering, financial innovation, fixed income, Glass-Steagall Act, information asymmetry, invisible hand, Isaac Newton, iterative process, junk bonds, Long Term Capital Management, margin call, money market fund, moral hazard, mortgage debt, place-making, Ponzi scheme, prediction markets, proprietary trading, risk tolerance, Savings and loan crisis, shareholder value, statistical model, The Wealth of Nations by Adam Smith, time value of money, too big to fail, web of trust

Rating agencies were confident of their models E1FPREF 06/16/2010 11:31:38 Page 19 Preface n xix used to assess the credit risk of subprime-loan pools because their methodologies had worked well in the (stable and benevolent) past. And here’s where regulation comes in. If you think that regulation in the form of ‘‘transparency’’ is sufficient on the grounds that the market can regulate itself as long as it has sufficient information, you place more faith in our ability to measure and predict market behavior than can reasonably be done. In a complex financial system, it’s difficult enough just to know who has sold credit default swaps to whom, let alone the consequences of their deterioration under specific market circumstances. Reforming the credit default swap market by making their trading and ownership transparent may help to solve the first problem (though even this premise is somewhat doubtful, as one chapter in this book discusses), but it won’t do anything to solve the second.


pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency by Ian Demartino

3D printing, AltaVista, altcoin, bitcoin, Bitcoin Ponzi scheme, blockchain, buy low sell high, capital controls, cloud computing, Cody Wilson, corporate governance, crowdsourcing, cryptocurrency, decentralized internet, distributed ledger, Dogecoin, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, fiat currency, Firefox, forensic accounting, global village, GnuPG, Google Earth, Haight Ashbury, initial coin offering, Jacob Appelbaum, Kevin Kelly, Kickstarter, litecoin, M-Pesa, Marc Andreessen, Marshall McLuhan, Oculus Rift, peer-to-peer, peer-to-peer lending, Ponzi scheme, prediction markets, printed gun, QR code, ransomware, Ross Ulbricht, Salesforce, Satoshi Nakamoto, self-driving car, selling pickaxes during a gold rush, Skype, smart contracts, Steven Levy, the medium is the message, underbanked, WikiLeaks, Zimmermann PGP

Ethereum is more than a coin, describing itself thus: “Ethereum is a decentralized platform that runs smart contracts: applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference. Ethereum is how the Internet was supposed to work.” As with Nxt and CounterParty, it allows for asset/token creation. But more than that, Ethereum has its own programming language built in, which has resulted in some very exciting projects. These include Augur, a crowd-powered prediction market where people with the Augur coin are incentivized to vote on whether a prediction came true or not. (How this works and how it keeps people honest is beyond the scope of this chapter.) There is a Bitcoin-like coin in Ethereum, called Ether, that makes the system run, secures the network and settles contracts.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

There is an interesting difference of opinion over how much data should be used based on the nature of the market in which the organization operates. While conventional wisdom says to gather as much data as possible (hence the term Big Data), psychologist Gerd Gigerenzer cautions that in uncertain markets, it is better to simplify, use heuristics and rely on fewer variables. In stable and predictable markets, on the other hand, he recommends organizations “complexify” and use algorithms with more variables. One of the leaders in gleaning insights from massive amounts of data is Palantir. Founded in 2004, Palantir builds government, commercial and health software solutions that empower organizations to make sense of disparate data.


pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, Apollo 13, backtesting, Bear Stearns, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, call centre, Charles Babbage, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, financial engineering, Flash crash, G4S, Gödel, Escher, Bach, Hacker News, High speed trading, Howard Rheingold, index fund, Isaac Newton, Jim Simons, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, Max Levchin, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, proprietary trading, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

In the case of a stock trader, an algorithm that seeks to capture the spread between a stock’s bid and offer prices can be thwarted when the market moves, which can leave the trader and his algorithm with only one side—the wrong side—of a trade. If an algorithm bought Microsoft at the bid of $50.00 and then failed to sell it within the same several seconds at the ask of $50.02 before the market went down, the trader will be taking a loss when he sells at the new ask of $49.99. These algorithms are built to predict market direction. To be successful, the algorithm has to be right only 51 percent of the time. But a 51 percent probability could still leave a high-frequency trader open to big losses if he trades only ten times a day. There could be plenty of days where he loses on seven of ten trades. But high-frequency traders are called “high frequency” for a reason.


Know Thyself by Stephen M Fleming

Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, autism spectrum disorder, autonomous vehicles, availability heuristic, backpropagation, citation needed, computer vision, confounding variable, data science, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, Dunning–Kruger effect, Elon Musk, Estimating the Reproducibility of Psychological Science, fake news, global pandemic, higher-order functions, index card, Jeff Bezos, l'esprit de l'escalier, Lao Tzu, lifelogging, longitudinal study, meta-analysis, mutually assured destruction, Network effects, patient HM, Pierre-Simon Laplace, power law, prediction markets, QWERTY keyboard, recommendation engine, replication crisis, self-driving car, side project, Skype, Stanislav Petrov, statistical model, theory of mind, Thomas Bayes, traumatic brain injury

By uploading a time-stamped document outlining the predictions and rationale for an experiment (known as preregistration), scientists can keep themselves honest and avoid making up stories to explain sets of flaky findings. There is also encouraging data that shows that when scientists own up about getting things wrong, the research community responds positively, seeing them as more collegiate and open rather than less competent.13 Another line of work is aiming to create “prediction markets” where researchers can bet on which findings they think will replicate. The Social Sciences Replication Project team set up a stock exchange, in which volunteers could buy or sell shares in each study under scrutiny, based on how reproducible they expected it to be. Each participant in the market started out with $100, and their final earnings were determined by how much they bet on the findings that turned out to replicate.


pages: 332 words: 91,780

Starstruck: The Business of Celebrity by Currid

barriers to entry, Bernie Madoff, Big Tech, Donald Trump, income inequality, index card, industrial cluster, Mark Zuckerberg, Metcalfe’s law, natural language processing, place-making, Ponzi scheme, post-industrial society, power law, prediction markets, public intellectual, Renaissance Technologies, Richard Florida, Robert Metcalfe, Robert Solow, rolodex, search costs, shareholder value, Silicon Valley, slashdot, Stephen Fry, the long tail, The Theory of the Leisure Class by Thorstein Veblen, transaction costs, Tyler Cowen, upwardly mobile, urban decay, Vilfredo Pareto, Virgin Galactic, winner-take-all economy

(Studios at least try to remain unconvinced in order to keep the salaries down.)56 If you believe Anita Elberse, a star is worth approximately $3 million in box office revenue. Elberse studied twelve hundred film-casting announcements and observed the influence of new information about a film (e.g., Matt Damon starring in the latest Bourne film or Julia Roberts joining Ocean’s Eleven) on the Hollywood Stock Exchange, an online prediction market that looks at a film’s expected revenue based on casting and financial decisions. Elberse found that the stars’ biggest impact is their ability to draw other stars to act in the film and to drum up financial backing for production. Stars do not, however, increase a studio’s valuation. Stars certainly obtain more value from their paycheck than the studios get in ticket sales.


pages: 291 words: 91,783

Griftopia: Bubble Machines, Vampire Squids, and the Long Con That Is Breaking America by Matt Taibbi

addicted to oil, affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Bear Stearns, Bernie Sanders, Bretton Woods, buy and hold, carried interest, classic study, clean water, collateralized debt obligation, collective bargaining, computerized trading, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, David Brooks, desegregation, diversification, diversified portfolio, Donald Trump, financial innovation, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, Greenspan put, illegal immigration, interest rate swap, laissez-faire capitalism, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, medical malpractice, military-industrial complex, money market fund, moral hazard, mortgage debt, Nixon triggered the end of the Bretton Woods system, obamacare, passive investing, Ponzi scheme, prediction markets, proprietary trading, prudent man rule, quantitative easing, reserve currency, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, sovereign wealth fund, too big to fail, trickle-down economics, Y2K, Yom Kippur War

By October, with the U.S. in the sixth of what would ultimately be ten consecutive months of job losses, Greenspan remained stubborn. “The economy,” he said, “has not yet slipped into recession.” The economy has a lot in common with the weather, and even very good economists charged with the job of predicting market swings can become victims of unexpected turns, just like meteorologists. But Greenspan’s errors were often historic, idiotic blunders, evidence of a fundamental misunderstanding of problems that led to huge disasters. In fact, if you dig under almost every one of the major financial crashes of our time, you can find some kind of Greenspan quote cheerfully telling people not to worry about where the new trends in the economy were leading.


pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis

"World Economic Forum" Davos, 23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, AOL-Time Warner, barriers to entry, Berlin Wall, bike sharing, business process, call centre, carbon tax, cashless society, citizen journalism, clean water, commoditize, connected car, content marketing, credit crunch, crowdsourcing, death of newspapers, different worldview, disintermediation, diversified portfolio, don't be evil, Dunbar number, fake news, fear of failure, Firefox, future of journalism, G4S, Golden age of television, Google Earth, Googley, Howard Rheingold, informal economy, inventory management, Jeff Bezos, jimmy wales, John Perry Barlow, Kevin Kelly, Marc Benioff, Mark Zuckerberg, moral hazard, Network effects, new economy, Nicholas Carr, old-boy network, PageRank, peer-to-peer lending, post scarcity, prediction markets, pre–internet, Ronald Coase, Salesforce, search inside the book, Sheryl Sandberg, Silicon Valley, Skype, social graph, social software, social web, spectrum auction, speech recognition, Steve Jobs, the long tail, the medium is the message, The Nature of the Firm, the payments system, The Wisdom of Crowds, transaction costs, web of trust, WikiLeaks, Y Combinator, Zipcar

Yes, speculators could arbitrage seats, but if they’re paid-for and nonrefundable, what problem is that for the airline? Resellers become market makers. This exchange sets a new market value for seats that in some cases will be higher than the airlines’ own fares. The airline could use the exchange as a prediction market to forecast and maximize load. It might see a surge in demand for a destination, perhaps for reasons it could not predict (a new conference or festival, good media coverage for a getaway, a travel bargain, or currency fluctuations unleashing pent-up demand). With sufficient notice, the airline could add capacity, which would keep it ahead of arbitrageurs.


pages: 313 words: 95,077

Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky

Andrew Keen, Andy Carvin, Berlin Wall, bike sharing, bioinformatics, Brewster Kahle, c2.com, Charles Lindbergh, commons-based peer production, crowdsourcing, digital rights, en.wikipedia.org, Free Software Foundation, Garrett Hardin, hiring and firing, hive mind, Howard Rheingold, Internet Archive, invention of agriculture, invention of movable type, invention of the printing press, invention of the telegraph, jimmy wales, John Perry Barlow, Joi Ito, Kuiper Belt, liberation theology, Mahatma Gandhi, means of production, Merlin Mann, Metcalfe’s law, Nash equilibrium, Network effects, Nicholas Carr, Picturephone, place-making, Pluto: dwarf planet, power law, prediction markets, price mechanism, prisoner's dilemma, profit motive, Richard Stallman, Robert Metcalfe, Ronald Coase, Silicon Valley, slashdot, social software, Stewart Brand, supply-chain management, the Cathedral and the Bazaar, the long tail, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, ultimatum game, Vilfredo Pareto, Wayback Machine, Yochai Benkler, Yogi Berra

In his book of that name he identified the ways distributed groups whose members aren’t connected can often generate better answers, by pooling their knowledge or intuition without having to come to an agreement. We have many ways of achieving this kind of aggregation, from market pricing mechanisms to voting to the prediction markets Surowiecki champions, but these methods all have two common characteristics: they work better in large groups, and they don’t require direct communication as the norm among members. (Indeed, in the case of markets, such communication is often forbidden, on the grounds that small clusters of collaborators can actually pervert the workings of the large system.)


pages: 357 words: 91,331

I Will Teach You To Be Rich by Sethi, Ramit

Albert Einstein, asset allocation, buy and hold, buy low sell high, diversification, diversified portfolio, do what you love, geopolitical risk, index fund, John Bogle, late fees, low interest rates, money market fund, mortgage debt, mortgage tax deduction, Paradox of Choice, prediction markets, random walk, risk tolerance, Robert Shiller, shareholder value, Silicon Valley, survivorship bias, the rule of 72, Vanguard fund

See Credit scores Fidelity, 187, 192 Financial advisers, 153–55 Financial expertise, 143–58 active vs. passive management and, 155–58 engineering a perfect stock-picking record and, 151 legendary investors and, 149 market-timing newsletters and, 145 personal-finance blogs and, 152 pundits’ and fund managers’ inability to predict market and, 2–3, 145–50, 165, 168 ratings of stocks and funds and, 148–52 529s, 217 Fixed costs, 104–6, 107, 130 Flexo, 44–45 401(k)s, 77–82, 176 amount to contribute to, 76, 77, 89 automatic contributions to, 79–80, 82, 129, 132, 136 common concerns about, 80–81 early withdrawal of money from, 80, 81, 85, 212 employer match and, 71, 76, 78, 79, 81, 82, 89 investing money in, 4, 81, 83, 185–86, 189, 198, 201, 209 paying credit card debt with, 46 setting up, 77, 82, 90 statistics on, 71, 72 switching jobs and, 80–81 tax-deferred growth of, 78, 80, 81, 210, 211, 221 Freelancing, 120, 139–41 Conscious Spending Plan and, 141 quarterly estimated tax payments and, 135 Friends, money issues with, 221 Frugality: cheapness vs., 94–96 prioritizing spending and, 97 Fund managers, poor performance of, 144–51, 155 Fun money, 107, 108, 130 g Get Rich Slowly, 152 Gifts, saving money for, 106–7 Girlfriends.


pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango

algorithmic trading, automated trading system, backtesting, buy and hold, commodity trading advisor, Credit Default Swap, Elliott wave, financial engineering, fixed income, global macro, Long Term Capital Management, managed futures, Market Wizards by Jack D. Schwager, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game

But it doesn’t matter what the reason is. The action is what counts. The action that’s required is other people have to pay higher prices. As soon as other people—fund managers, hedge funds, whoever these people are—decide to not pay higher prices, stock XYZ will no longer go up. In order to predict markets, the people who really figured out the game are predicting people, and I can tell you that some of the more successful hedge funds understand this. They’re looking at the market not so much through fundamental analysis and technical analysis. They are trying to sense it. They want to predict what people are going to do.


pages: 345 words: 87,745

The Power of Passive Investing: More Wealth With Less Work by Richard A. Ferri

Alan Greenspan, asset allocation, backtesting, Benchmark Capital, Bernie Madoff, book value, buy and hold, capital asset pricing model, cognitive dissonance, correlation coefficient, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, John Bogle, junk bonds, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, Ponzi scheme, prediction markets, proprietary trading, prudent man rule, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, Tax Reform Act of 1986, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

TrimTabs Investment Research, a consolidator of mutual fund flow data, concluded that equity prices tend to fall after equity exchange-traded funds (ETFs) rake in large sums of money and rise after equity ETFs post heavy outflows. Regression analysis suggests the probability that equity ETF flows are a contrary leading indicator of equity prices is more than 99 percent. This means the flow of ETF money predicts market changes with high accuracy—in the opposite direction!6 One mutual fund cash flow study after another has consistently shown the same performance chasing phenomenon. Fund styles with superior performance and high fund ratings raked in the most money, and this usually occurs close to the time when these investment styles peak in performance.


pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Buterin offers the analogy of a vending machine, but any similar stepwise tree algorithm applies (if you insert the correct coin and if you designate choice of purchase, then you can collect widget in slot below; if not, you can pound machine fecklessly with your fists). As Buterin declared in announcing his system, he expected Ethereum to enable “protocols around decentralized file storage, decentralized computing and prediction markets, and provide a massive boost to other peer-to-peer protocols by adding an economic layer.” Most of the other crypto-ventures have used this more resourceful Ethereum blockchain and Solidity language to build their infrastructures. For reach and ingenuity, it is hard to excel Golem. Calling itself, with partial felicity, an “Airbnb for computers,” it offers to rent your computer’s resources when you are not using them.


pages: 297 words: 103,910

Free culture: how big media uses technology and the law to lock down culture and control creativity by Lawrence Lessig

Brewster Kahle, Cass Sunstein, content marketing, creative destruction, digital divide, Free Software Foundation, future of journalism, George Akerlof, Innovator's Dilemma, Internet Archive, invention of the printing press, Joi Ito, Kenneth Arrow, Kevin Kelly, knowledge economy, Louis Daguerre, machine readable, new economy, prediction markets, prisoner's dilemma, profit motive, rent-seeking, Richard Florida, Richard Stallman, Ronald Coase, Ronald Reagan, Saturday Night Live, Silicon Valley, software patent, synthetic biology, transaction costs

Because here we have hundreds of thousands of webcasters who want to pay, and that should establish the market rate, and if you set the rate so high, you're going to drive the small webcasters out of business… ." And the RIAA experts said, "Well, we don't really model this as an industry with thousands of webcasters, we think it should be an industry with, you know, five or seven big players who can pay a high rate and it's a stable, predictable market." (Emphasis added.) Translation: The aim is to use the law to eliminate competition, so that this platform of potentially immense competition, which would cause the diversity and range of content available to explode, would not cause pain to the dinosaurs of old. There is no one, on either the right or the left, who should endorse this use of the law.


pages: 417 words: 97,577

The Myth of Capitalism: Monopolies and the Death of Competition by Jonathan Tepper

"Friedman doctrine" OR "shareholder theory", Affordable Care Act / Obamacare, air freight, Airbnb, airline deregulation, Alan Greenspan, bank run, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, big-box store, Bob Noyce, Boston Dynamics, business cycle, Capital in the Twenty-First Century by Thomas Piketty, citizen journalism, Clayton Christensen, collapse of Lehman Brothers, collective bargaining, compensation consultant, computer age, Cornelius Vanderbilt, corporate raider, creative destruction, Credit Default Swap, crony capitalism, diversification, don't be evil, Donald Trump, Double Irish / Dutch Sandwich, Dunbar number, Edward Snowden, Elon Musk, en.wikipedia.org, eurozone crisis, Fairchild Semiconductor, Fall of the Berlin Wall, family office, financial innovation, full employment, gentrification, German hyperinflation, gig economy, Gini coefficient, Goldman Sachs: Vampire Squid, Google bus, Google Chrome, Gordon Gekko, Herbert Marcuse, income inequality, independent contractor, index fund, Innovator's Dilemma, intangible asset, invisible hand, Jeff Bezos, Jeremy Corbyn, Jevons paradox, John Nash: game theory, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Rogoff, late capitalism, London Interbank Offered Rate, low skilled workers, Mark Zuckerberg, Martin Wolf, Maslow's hierarchy, means of production, merger arbitrage, Metcalfe's law, multi-sided market, mutually assured destruction, Nash equilibrium, Network effects, new economy, Northern Rock, offshore financial centre, opioid epidemic / opioid crisis, passive investing, patent troll, Peter Thiel, plutocrats, prediction markets, prisoner's dilemma, proprietary trading, race to the bottom, rent-seeking, road to serfdom, Robert Bork, Ronald Reagan, Sam Peltzman, secular stagnation, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Skype, Snapchat, Social Responsibility of Business Is to Increase Its Profits, SoftBank, Steve Jobs, stock buybacks, tech billionaire, The Chicago School, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, undersea cable, Vanguard fund, vertical integration, very high income, wikimedia commons, William Shockley: the traitorous eight, you are the product, zero-sum game

If the original incarnations of Communism under Lenin, Stalin, and Mao failed because central planning was a disaster, Big Data will now come to the rescue. Last year, Jack Ma, founder of Alibaba, the online platform with over half a billion users, argued, “Big Data will make the market smarter and make it possible to plan and predict market forces so as to allow us to finally achieve a planned economy.”2 The 2013 revelations of Edward Snowden exposed the involvement of American companies and intelligence agencies in programs that gave the government access to personal data. Americans were briefly outraged and then continued their lives as they did before.


pages: 335 words: 97,468

Uncharted: How to Map the Future by Margaret Heffernan

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, Anne Wojcicki, anti-communist, Atul Gawande, autonomous vehicles, banking crisis, Berlin Wall, Boris Johnson, Brexit referendum, chief data officer, Chris Urmson, clean water, complexity theory, conceptual framework, cosmic microwave background, creative destruction, CRISPR, crowdsourcing, data science, David Attenborough, discovery of penicillin, driverless car, epigenetics, Fall of the Berlin Wall, fear of failure, George Santayana, gig economy, Google Glasses, Greta Thunberg, Higgs boson, index card, Internet of things, Jaron Lanier, job automation, Kickstarter, Large Hadron Collider, late capitalism, lateral thinking, Law of Accelerating Returns, liberation theology, mass immigration, mass incarceration, megaproject, Murray Gell-Mann, Nate Silver, obamacare, oil shale / tar sands, passive investing, pattern recognition, Peter Thiel, prediction markets, RAND corporation, Ray Kurzweil, Rosa Parks, Sam Altman, scientific management, Shoshana Zuboff, Silicon Valley, smart meter, Stephen Hawking, Steve Ballmer, Steve Jobs, surveillance capitalism, TED Talk, The Signal and the Noise by Nate Silver, Tim Cook: Apple, twin studies, University of East Anglia

In 1993, he predicted that Clinton’s tax rises would squash economic growth – and when the late 1990s boom ensued, attributed it to Reagan’s tax cuts back in the early 1980s. Ten years later, he was rewarded for his intellectual intransigence when President Trump made him director of the National Economic Council.14 Incomplete, ideological and self-interested: the harder economists try to identify sure-fire methods of predicting markets, the more such insight eludes them. The rise of big data and artificial intelligence enables firms to analyse quantities of information far beyond the dreams of earlier forecasters: not just prices, money supply and national economies but satellite images tracking the number of oil tankers on the seas or cars at shopping malls.


pages: 367 words: 97,136

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation by Sebastien Page

Andrei Shleifer, asset allocation, backtesting, Bernie Madoff, bitcoin, Black Swan, Bob Litterman, book value, business cycle, buy and hold, Cal Newport, capital asset pricing model, commodity super cycle, coronavirus, corporate governance, COVID-19, cryptocurrency, currency risk, discounted cash flows, diversification, diversified portfolio, en.wikipedia.org, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, future of work, Future Shock, G4S, global macro, implied volatility, index fund, information asymmetry, iterative process, loss aversion, low interest rates, market friction, mental accounting, merger arbitrage, oil shock, passive investing, prediction markets, publication bias, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, robo advisor, seminal paper, shareholder value, Sharpe ratio, sovereign wealth fund, stochastic process, stochastic volatility, stocks for the long run, systematic bias, systematic trading, tail risk, transaction costs, TSMC, value at risk, yield curve, zero-coupon bond, zero-sum game

They are useful to the extent they help formulate a view about the future. It’s in that context that I review various approaches to expected returns in this chapter. Note 1. A “quant” is an industry term used to describe those involved in quantitative research and investment management. Quants use computer models and mathematics to predict markets and manage risk. 1 Equilibrium and Something About Quack Remedies Sold on the Internet The expected return for the market portfolio should be a function of the level of interest rates. When rates are high, investors require higher returns on all tradable assets. —JPP A note to readers: I completed this book shortly before the Covid crisis.


pages: 329 words: 99,504

Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud by Ben McKenzie, Jacob Silverman

algorithmic trading, asset allocation, bank run, barriers to entry, Ben McKenzie, Bernie Madoff, Big Tech, bitcoin, Bitcoin "FTX", blockchain, capital controls, citizen journalism, cognitive dissonance, collateralized debt obligation, COVID-19, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, cryptocurrency, data science, distributed ledger, Dogecoin, Donald Trump, effective altruism, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, experimental economics, financial deregulation, financial engineering, financial innovation, Flash crash, Glass-Steagall Act, high net worth, housing crisis, information asymmetry, initial coin offering, Jacob Silverman, Jane Street, low interest rates, Lyft, margin call, meme stock, money market fund, money: store of value / unit of account / medium of exchange, Network effects, offshore financial centre, operational security, payday loans, Peter Thiel, Ponzi scheme, Potemkin village, prediction markets, proprietary trading, pushing on a string, QR code, quantitative easing, race to the bottom, ransomware, regulatory arbitrage, reserve currency, risk tolerance, Robert Shiller, Robinhood: mobile stock trading app, Ross Ulbricht, Sam Bankman-Fried, Satoshi Nakamoto, Saturday Night Live, short selling, short squeeze, Silicon Valley, Skype, smart contracts, Steve Bannon, systems thinking, TikTok, too big to fail, transaction costs, tulip mania, uber lyft, underbanked, vertical integration, zero-sum game

Binance: Ben McKenzie and Jacob Silverman, “Why users are pushing back against the world’s largest crypto exchange,” Washington Post, April 1, 2022. 92 crypto took off in China . . . to avoid capital controls: Karen Yeung, “Cryptocurrencies help Chinese evade capital and currency controls in moving billions overseas,” South China Morning Post, August 26, 2020. 92 Binance has . . . no headquarters: Patricia Kowsmann and Caitlan Ostroff, “$76 Billion a Day: How Binance Became the World’s Biggest Crypto Exchange,” Wall Street Journal, November 11, 2021. 100 Peter Thiel, the arch-capitalist: Abram Brown, “Peter Thiel Pumps Bitcoin, Calls Warren Buffett A ‘Sociopathic Grandpa,’ ” Forbes.com, April 7, 2022. 102 “In Miami we have big balls”: Daniel Kuhn, “The Meaning of Miami’s Castrated Bitcoin Bull,” CoinDesk, April 8, 2022. 103 It was Brock Pierce: Interview with Brock Pierce, Bitcoin 2022 (Miami, FL), April 7, 2022. 106 Eric Adams. . . . in the form of Bitcoin: Ben McKenzie and Jacob Silverman, “The Embarrassment of New York’s Next Mayor Taking His Paychecks in Bitcoin,” Slate, November 5, 2021. 109 O’Leary was particularly bullish: Adam Morgan McCarthy, “‘Spigots of capital’ will flood into crypto once policy and regulation are set, ‘Shark Tank’ investor Kevin O’Leary predicts,” Markets Insider (Insider.com), April 7, 2022. 109 Mario Gomez and Carmen Valeria Escobar: Interviews with Mario Gomez and Carmen Escobar, Bitcoin 2022 (Miami, FL), April 9, 2022. CHAPTER 7: THE WORLD’S COOLEST DICTATOR 114 brash young politician named Bukele: Gabriel Labrador, “How Bukele Crafted a Best-Selling Political Brand,” El Faro, May 3, 2022. 119 rollout of the government’s Chivo Wallet system: Jacob Silverman and Ben McKenzie, “Nayib Bukele’s Broken Bitcoin Promise,” The Intercept, July 22, 2022. 120 a horrific spree of gang violence: Associated Press, “El Salvador locks down prisons after wave of 87 killings over weekend,” Guardian, March 28, 2022. 120 Mario Garcia was one: Interview with Mario Garcia, El Zonte (Chiltiupán, El Salvador), May 16, 2022. 122 Bitcoin City: Edward Ongweso Jr., “El Salvador’s President Unveils Golden ‘Bitcoin City’ Amid Brutal Crash,” Motherboard (Tech by Vice), May 10, 2022. 123 Wilfredo Claros: Interview with Wilfredo Claros, Condadillo (La Unión, El Salvador), May 17, 2022. 124 admitted to having forged a secret deal: Carlos Martínez, “Collapsed Government Talks with MS - 13 Sparked Record Homicides in El Salvador, Audios Reveal,” El Faro, May 17, 2022.


pages: 340 words: 101,675

A New History of the Future in 100 Objects: A Fiction by Adrian Hon

Adrian Hon, air gap, Anthropocene, augmented reality, blockchain, bounce rate, call centre, carbon credits, carbon tax, Cepheid variable, charter city, Clayton Christensen, clean water, cognitive dissonance, congestion charging, creative destruction, CRISPR, crowdsourcing, cryptocurrency, deepfake, defense in depth, discrete time, disinformation, disintermediation, driverless car, drone strike, food desert, game design, gamification, gravity well, hive mind, hydroponic farming, impulse control, income inequality, job automation, Kickstarter, Kim Stanley Robinson, knowledge worker, life extension, lifelogging, low earth orbit, machine translation, MITM: man-in-the-middle, moral panic, Neal Stephenson, no-fly zone, off grid, offshore financial centre, oil shale / tar sands, orbital mechanics / astrodynamics, peak oil, peer-to-peer, phenotype, planned obsolescence, post scarcity, precariat, precautionary principle, prediction markets, rewilding, Silicon Valley, skeuomorphism, Skype, smart contracts, social graph, South Sea Bubble, speech recognition, stem cell, Stewart Brand, synthetic biology, technoutopianism, telepresence, transfer pricing, tulip mania, Turing test, urban sprawl, Vernor Vinge, VTOL, working-age population

Nadia 91    MORAL AGENTS United States, 2057 Depending on whom you asked, the Selinger incident was either an outrage or an overreaction. But while the answer was divisive, everyone agreed on the question: were moral agents usurping human virtue? In 2057, the Bautista Humanitarian Award was given to Luciano Selinger. The front-runner in prediction markets for weeks, Selinger’s tireless work on improving living standards for the displaced inhabitants of the global warming-ravaged US Midwest was without peer. At twenty-eight, Selinger was considerably younger than the other nominees, but his entire life seemed as if it were an arrow shot from a bow.


Global Catastrophic Risks by Nick Bostrom, Milan M. Cirkovic

affirmative action, agricultural Revolution, Albert Einstein, American Society of Civil Engineers: Report Card, anthropic principle, artificial general intelligence, Asilomar, availability heuristic, backpropagation, behavioural economics, Bill Joy: nanobots, Black Swan, carbon tax, carbon-based life, Charles Babbage, classic study, cognitive bias, complexity theory, computer age, coronavirus, corporate governance, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, death of newspapers, demographic transition, Deng Xiaoping, distributed generation, Doomsday Clock, Drosophila, endogenous growth, Ernest Rutherford, failed state, false flag, feminist movement, framing effect, friendly AI, Georg Cantor, global pandemic, global village, Great Leap Forward, Gödel, Escher, Bach, Hans Moravec, heat death of the universe, hindsight bias, information security, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Kevin Kelly, Kuiper Belt, Large Hadron Collider, launch on warning, Law of Accelerating Returns, life extension, means of production, meta-analysis, Mikhail Gorbachev, millennium bug, mutually assured destruction, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, Oklahoma City bombing, P = NP, peak oil, phenotype, planetary scale, Ponzi scheme, power law, precautionary principle, prediction markets, RAND corporation, Ray Kurzweil, Recombinant DNA, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, Singularitarianism, social intelligence, South China Sea, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, the long tail, The Turner Diaries, Tunguska event, twin studies, Tyler Cowen, uranium enrichment, Vernor Vinge, War on Poverty, Westphalian system, Y2K

Introduction 29 The fruitfulness of further work on global catastrophic risk will, we believe, be enhanced if it gives consideration to the following suggestions: • I n the study of individual risks, focus more on producing actionable information such as early-warning signs, metrics for measuring progress towards risk reduction, and quantitative models for risk assessment. • Develop and implement better methodologies and institutions for information aggregation and probabilistic forecasting, such as prediction markets. • Put more effort into developing and evaluating possible mitigation strategies, both because of the direct utility of such research and because a concern with the policy instruments with which a risk can be influenced is likely to enrich our theoretical understanding of the nature of the risk

His major fields of interest include health policy, regulation, and formal political theory. He is known as an expert on idea futures markets and was involved in the creation ofthe Foresight Exchange and DARPA's FutureMAP project. He is also known for inventing Market Scoring Rules such as LM S R (Logarithmic Market Scoring Rule) used by prediction markets such as Inkling Markets and Washington Stock Exchange, and has conducted research on signalling. James J. Hughes is a bioethicist and sociologist at Trinity College in Hartford, Connecticut, where he teaches health policy. He holds a doctorate in sociology from the University of Chicago, where he also taught bioethics and health policy at the MacLean Center for Clinical Medical Ethics.


pages: 354 words: 105,322

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black Swan, blockchain, Boeing 747, Bonfire of the Vanities, Bretton Woods, Brexit referendum, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, driverless car, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, Glass-Steagall Act, global macro, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, low interest rates, machine readable, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, Minsky moment, Money creation, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, operational security, Paul Samuelson, Peace of Westphalia, Phillips curve, Pierre-Simon Laplace, plutocrats, prediction markets, price anchoring, price stability, proprietary trading, public intellectual, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk free rate, risk-adjusted returns, Robert Solow, Ronald Reagan, Savings and loan crisis, Silicon Valley, sovereign wealth fund, special drawing rights, stock buybacks, stocks for the long run, tech billionaire, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, We are all Keynesians now, Westphalian system

Complexity’s essence is that invisible changes in initial conditions produce radically different systemic outcomes. Market processes are nonlinear and practically nondeterministic. There may be a cause-and-effect relationship between catalyst and collapse. Still, it is too small to observe and the timing is difficult to forecast. Predicting market crashes is like predicting earthquakes. One may be certain the event will occur, and can estimate its magnitude, yet one will never know exactly when. Laboratory science, in particular sand pile experiments (similar to a snowflake-avalanche dynamic) and computer simulations using cellular automata, reveal degree distributions of extreme events.


pages: 437 words: 105,934

#Republic: Divided Democracy in the Age of Social Media by Cass R. Sunstein

A Declaration of the Independence of Cyberspace, affirmative action, Affordable Care Act / Obamacare, Alvin Toffler, behavioural economics, Bernie Sanders, Black Lives Matter, Cass Sunstein, choice architecture, digital divide, Donald Trump, drone strike, Erik Brynjolfsson, fake news, Filter Bubble, friendly fire, global village, illegal immigration, immigration reform, income inequality, Jane Jacobs, John Perry Barlow, loss aversion, Mark Zuckerberg, obamacare, Oklahoma City bombing, prediction markets, road to serfdom, Ronald Reagan, Silicon Valley, Skype, Snapchat, stem cell, The Chicago School, The Death and Life of Great American Cities, the long tail, The Wisdom of Crowds, Twitter Arab Spring, WikiLeaks, Yochai Benkler

In this way, social media have a powerful democratizing function.16 Countless websites are now aggregating diverse knowledge. For diverse products—books, movies, cars, doctors, and computers—it is easy to find sources that tell you what most people think, and it is easy as well to contribute to that collective knowledge. Prediction markets, for example, aggregate the judgments of numerous forecasters, and they are proving to be remarkably accurate. There is much to be said about the growing ability of consumers to be producers too.17 But that is not my topic here. I will provide little discussion of monopolistic behavior by suppliers, or manipulative practices by them.


The Permanent Portfolio by Craig Rowland, J. M. Lawson

Alan Greenspan, Andrei Shleifer, asset allocation, automated trading system, backtesting, bank run, banking crisis, Bear Stearns, Bernie Madoff, buy and hold, capital controls, correlation does not imply causation, Credit Default Swap, currency risk, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, high net worth, High speed trading, index fund, inflation targeting, junk bonds, low interest rates, margin call, market bubble, money market fund, new economy, passive investing, Ponzi scheme, prediction markets, risk tolerance, stocks for the long run, survivorship bias, technology bubble, transaction costs, Vanguard fund

The entire universe of potential future economic environments is actually quite small. The economy is a bit like the weather—although it is driven by an almost infinitely complex set of processes, it manifests itself in the form of climatic conditions in which it is always either hot or cold and wet or dry. Rather than attempting to overcome uncertainty through predictions, market analysis, and punditry, the Permanent Portfolio strategy embraces the concept of uncertainty in all human affairs and assumes that uncertainty will always be present, and that this uncertainty will manifest itself through changes in the economy. This aspect of the Permanent Portfolio is one of the features that most distinguishes it from other approaches to investing.


The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

Information technology such as block chain, which is a secure Internet ledger that replaces financial middlemen in transactions, is being tested on a small scale but could soon disrupt multitrillion-dollar financial markets. Machine learning is being used to improve credit evaluation on loans, to accurately deliver business and financial information, to pick up signals on The Rise of Machine Learning 15 social media that predict market trends, and to provide biometric security for financial transactions. Whoever has the most data wins, and the world is awash with financial data. Learning the Law Deep learning is just beginning to affect the legal profession. Much of the routine work of associates in law firms who charge hundreds of dollar an hour will be automated, especially in large, high-value commercial offices.


pages: 406 words: 109,794

Range: Why Generalists Triumph in a Specialized World by David Epstein

Airbnb, Albert Einstein, Apollo 11, Apple's 1984 Super Bowl advert, Atul Gawande, Checklist Manifesto, Claude Shannon: information theory, Clayton Christensen, clockwork universe, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, deep learning, deliberate practice, Exxon Valdez, fail fast, Flynn Effect, Freestyle chess, functional fixedness, game design, Gene Kranz, Isaac Newton, Johannes Kepler, knowledge economy, language acquisition, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, messenger bag, meta-analysis, Mikhail Gorbachev, multi-armed bandit, Nelson Mandela, Netflix Prize, pattern recognition, Paul Graham, precision agriculture, prediction markets, premature optimization, pre–internet, random walk, randomized controlled trial, retrograde motion, Richard Feynman, Richard Feynman: Challenger O-ring, Silicon Valley, Silicon Valley billionaire, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, sunk-cost fallacy, systems thinking, Walter Mischel, Watson beat the top human players on Jeopardy!, Y Combinator, young professional

Every team member still had to make individual predictions, but the team was scored by collective performance. On average, forecasters on the small superteams became 50 percent more accurate in their individual predictions. Superteams beat the wisdom of much larger crowds—in which the predictions of a large group of people are averaged—and they also beat prediction markets, where forecasters “trade” the outcomes of future events like stocks, and the market price represents the crowd prediction. It might seem like the complexity of predicting geopolitical and economic events would necessitate a group of narrow specialists, each bringing to the team extreme depth in one area.


pages: 440 words: 108,137

The Meritocracy Myth by Stephen J. McNamee

Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, American ideology, antiwork, Bernie Madoff, British Empire, business cycle, classic study, collective bargaining, computer age, conceptual framework, corporate governance, deindustrialization, delayed gratification, demographic transition, desegregation, deskilling, Dr. Strangelove, equal pay for equal work, estate planning, failed state, fixed income, food desert, Gary Kildall, gender pay gap, Gini coefficient, glass ceiling, helicopter parent, income inequality, informal economy, invisible hand, job automation, joint-stock company, junk bonds, labor-force participation, longitudinal study, low-wage service sector, marginal employment, Mark Zuckerberg, meritocracy, Michael Milken, mortgage debt, mortgage tax deduction, new economy, New Urbanism, obamacare, occupational segregation, old-boy network, pink-collar, plutocrats, Ponzi scheme, post-industrial society, prediction markets, profit motive, race to the bottom, random walk, Savings and loan crisis, school choice, Scientific racism, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Spirit Level, the strength of weak ties, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, upwardly mobile, We are the 99%, white flight, young professional

The biggest difference between playing the stock market and other forms of gambling is that the odds for winning in the stock market are much better, although the return is usually much less. The odds of “winning” in the stock market can be increased beyond random chance by knowing as much as possible about the companies in which one invests and by being able to predict market changes. Knowing about companies and markets, in part, is a matter of social and cultural capital—being in a loop composed of people with accurate and current information. As recent investment scandals have underscored, these odds can be increased illegally through insider trading. Apart from such schemes, however, there is still an element of investor risk.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

Alan Greenspan, algorithmic trading, automated trading system, banking crisis, bash_history, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, financial engineering, fixed income, Flash crash, Ford Model T, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, information security, Jim Simons, Joseph Schumpeter, junk bonds, latency arbitrage, Long Term Capital Management, machine readable, Mark Zuckerberg, market design, market microstructure, Michael Milken, military-industrial complex, pattern recognition, payment for order flow, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, seminal paper, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, three-martini lunch, Tragedy of the Commons, transaction costs, uptick rule, Watson beat the top human players on Jeopardy!, zero-sum game

He was so skilled at discovering patterns in the market’s daily ebb and flow that he’d risen to the top of the trading world, working first at an elite Chicago firm, packed with math and physics Ph.D.s, called Hull Trading, then inside a top secret quantitative derivatives operation at Goldman Sachs, before taking over a powerful global desk at UBS, the giant Swiss bank. In 2007, he broke out on his own and convinced twenty-five top-notch traders, programmers, and quants (an industry term for mathematicians who use quantitative techniques to predict markets) from across Wall Street to join him. He set up shop in Stamford and launched Trading Machines just as signs emerged of an impending global financial crisis. It had amounted to one of the most ambitious trading projects outside a large investment bank in years. Despite the bad timing, Trading Machines had fared well in its debut, posting a tidy profit during a time when most of Wall Street was imploding.


pages: 422 words: 104,457

Dragnet Nation: A Quest for Privacy, Security, and Freedom in a World of Relentless Surveillance by Julia Angwin

AltaVista, Ayatollah Khomeini, barriers to entry, bitcoin, Chelsea Manning, Chuck Templeton: OpenTable:, clean water, crowdsourcing, cuban missile crisis, data is the new oil, David Graeber, Debian, disinformation, Edward Snowden, Filter Bubble, Firefox, Free Software Foundation, Garrett Hardin, GnuPG, Google Chrome, Google Glasses, Ida Tarbell, incognito mode, informal economy, Jacob Appelbaum, John Gilmore, John Markoff, Julian Assange, Laura Poitras, Marc Andreessen, market bubble, market design, medical residency, meta-analysis, mutually assured destruction, operational security, Panopticon Jeremy Bentham, prediction markets, price discrimination, randomized controlled trial, RFID, Robert Shiller, Ronald Reagan, security theater, Silicon Valley, Silicon Valley startup, Skype, smart meter, sparse data, Steven Levy, Tragedy of the Commons, Upton Sinclair, WikiLeaks, Y2K, zero-sum game, Zimmermann PGP

In 1996, self-proclaimed Internet anarchist Jim Bell posted on an Internet forum an essay titled “Assassination Politics,” describing how anonymous cash could enable the establishment of cash prizes to people who correctly “predict” somebody’s death. “It would be possible to make such awards in such a way so that nobody knows who is getting awarded the money, only that the award is being given.” Bell described this death prediction market as a way to punish “violators of rights” by putting a price on their heads. “Consider how history might have changed if we’d been able to ‘bump off’ Lenin, Stalin, Hitler, Mussolini, Tojo, Kim Il Sung, Ho Chi Minh, Ayatollah Khomeini, Saddam Hussein, Moammar Khadafi, and various others, along with all of their replacements if necessary, all for a measly few million dollars,” he wrote.


pages: 407 words: 104,622

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

Gann’s renown grew, based partly on a claim that, in a single month, he turned $130 into $12,000. Loyalists credited Gann with predicting everything from the Great Depression to the attack on Pearl Harbor. Gann concluded that a universal, natural order governed all facets of life—something he called the Law of Vibration—and that geometric sequences and angles could be used to predict market action. To this day, Gann analysis remains a reasonably popular branch of technical trading. Gann’s investing record was never substantiated, however, and his fans tended to overlook some colossal bloopers. In 1936, for example, Gann said, “I am confident the Dow Jones Industrial Average will never sell at 386 again,” meaning he was sure the Dow wouldn’t again reach that level, a prediction that didn’t quite stand the test of time.


pages: 405 words: 117,219

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis

3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, Bletchley Park, British Empire, business process, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, complexity theory, Computing Machinery and Intelligence, continuous integration, Conway's Game of Life, cosmological principle, dark matter, data science, deep learning, DeepMind, dematerialisation, double helix, Douglas Hofstadter, driverless car, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, index card, industrial robot, intentional community, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, Kickstarter, liberal capitalism, lifelogging, machine translation, millennium bug, mirror neurons, Moravec's paradox, natural language processing, Nick Bostrom, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, Plato's cave, post-industrial society, power law, precautionary principle, prediction markets, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Second Machine Age, self-driving car, seminal paper, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, Strategic Defense Initiative, strong AI, Stuart Kauffman, synthetic biology, systems thinking, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

As Artificial Intelligence evolves further, it will become the driver of a new machine age that could usher our species to new economic, social and technological heights. Supercomputers endowed with intelligence will be able to accurately model and simulate almost every natural process. We will acquire the power to engineer virtually everything: from new drugs to predicting markets and solving the problems of economic scarcity, to terraforming planets. Artificial Intelligence could make us virtually omnipotent. As citizens of a free society, we have a duty to come to terms with this future, and to understand and debate its moral, legal, political and ethical ramifications today.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

"World Economic Forum" Davos, AI winter, Alan Greenspan, algorithmic trading, AOL-Time Warner, Apollo 11, asset allocation, banking crisis, barriers to entry, Bear Stearns, Big bang: deregulation of the City of London, Bob Litterman, book value, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Charles Babbage, citizen journalism, collateralized debt obligation, Cornelius Vanderbilt, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, electricity market, Emanuel Derman, en.wikipedia.org, experimental economics, fake news, financial engineering, financial innovation, fixed income, Ford Model T, Gordon Gekko, Hans Moravec, Herman Kahn, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, Ivan Sutherland, Jim Simons, John Bogle, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, military-industrial complex, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, Savings and loan crisis, semantic web, Sharpe ratio, short selling, short squeeze, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, stock buybacks, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, tontine, too big to fail, transaction costs, Turing machine, two and twenty, Upton Sinclair, value at risk, value engineering, Vernor Vinge, Wayback Machine, yield curve, Yogi Berra, your tax dollars at work

Macroeconomic indicators like inflation, interest rates, housing starts, and oil reserves all seem reasonable in the right context. For an idea of the many ways to predict, look for books on regression analysis, times series methods, classification and regression trees (CARTs), neural nets, and wavelets, to name a few. This doesn’t touch ideas like collective intelligence and prediction markets, which bring people into the mix. Doing this correctly can put you on the fast track to that hockey rink in the yard or the Forbes 400, or both. Doing this incorrectly will keep you off the streets for a while, but will prove detrimental to your financial health if you follow your own advice.


The Economics Anti-Textbook: A Critical Thinker's Guide to Microeconomics by Rod Hill, Anthony Myatt

American ideology, Andrei Shleifer, Asian financial crisis, bank run, barriers to entry, behavioural economics, Bernie Madoff, biodiversity loss, business cycle, cognitive dissonance, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, different worldview, electricity market, endogenous growth, equal pay for equal work, Eugene Fama: efficient market hypothesis, experimental economics, failed state, financial innovation, full employment, gender pay gap, Gini coefficient, Glass-Steagall Act, Gunnar Myrdal, happiness index / gross national happiness, Home mortgage interest deduction, Howard Zinn, income inequality, indoor plumbing, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, liberal capitalism, low interest rates, low skilled workers, market bubble, market clearing, market fundamentalism, Martin Wolf, medical malpractice, military-industrial complex, minimum wage unemployment, moral hazard, Paradox of Choice, Pareto efficiency, Paul Samuelson, Peter Singer: altruism, positional goods, prediction markets, price discrimination, price elasticity of demand, principal–agent problem, profit maximization, profit motive, publication bias, purchasing power parity, race to the bottom, Ralph Nader, random walk, rent control, rent-seeking, Richard Thaler, Ronald Reagan, search costs, shareholder value, sugar pill, The Myth of the Rational Market, the payments system, The Spirit Level, The Wealth of Nations by Adam Smith, Thorstein Veblen, ultimatum game, union organizing, working-age population, World Values Survey, Yogi Berra

There is no strategic interaction between firms in monopolistic competition because they are all too small to pay attention to each other. In contrast, strat­ egic interaction is the core problem in the last industry structure – oligopoly. It ­occurs when there are relatively few firms in an industry, whether they differen­ tiate their products or not. Strategic interaction makes it the least predictable market structure of all, since no firm knows precisely how its rivals will react to any move it makes. The mainstream textbook presents some basic game theory to explain the conditions under which firms may collude to achieve the (shared) monopoly outcome. With only two firms the simple ‘prisoner’s dilemma’ is illustrated; neither firm trusts the other and any collusive agreement (or implicit cartel) invariably breaks down.


pages: 561 words: 120,899

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by Sharon Bertsch McGrayne

Abraham Wald, Alan Greenspan, Bayesian statistics, bioinformatics, Bletchley Park, British Empire, classic study, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, data science, double helix, Dr. Strangelove, driverless car, Edmond Halley, Fellow of the Royal Society, full text search, government statistician, Henri Poincaré, Higgs boson, industrial research laboratory, Isaac Newton, Johannes Kepler, John Markoff, John Nash: game theory, John von Neumann, linear programming, longitudinal study, machine readable, machine translation, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, Robert Mercer, Ronald Reagan, seminal paper, speech recognition, statistical model, stochastic process, Suez canal 1869, Teledyne, the long tail, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, We are all Keynesians now, Yom Kippur War

For several years, their Medallion Fund, limited to former and current employees, averaged annual returns of about 35%. The fund bought and sold shares so rapidly one day in 1997 that it accounted for more than 10% of all NASDAQ trades. To search for the nonrandom patterns and movements that will help predict markets, RenTech gathers as much information as possible. It begins with prior knowledge about the history of prices and how they fluctuate and correlate with each other. Then the company continuously updates that prior base. As Mercer explained, “RenTec gets a trillion bytes of data a day, from newspapers, AP wire, all the trades, quotes, weather reports, energy reports, government reports, all with the goal of trying to figure out what’s going to be the price of something or other at every point in the future. . . .


Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies by Jeremy J. Siegel

addicted to oil, Alan Greenspan, asset allocation, backtesting, behavioural economics, Black-Scholes formula, book value, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, cognitive dissonance, compound rate of return, correlation coefficient, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, Everybody Ought to Be Rich, fixed income, German hyperinflation, implied volatility, index arbitrage, index fund, Isaac Newton, it's over 9,000, John Bogle, joint-stock company, Long Term Capital Management, loss aversion, machine readable, market bubble, mental accounting, Money creation, Myron Scholes, new economy, oil shock, passive investing, Paul Samuelson, popular capitalism, prediction markets, price anchoring, price stability, proprietary trading, purchasing power parity, random walk, Richard Thaler, risk free rate, risk tolerance, risk/return, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, stock buybacks, stocks for the long run, subprime mortgage crisis, survivorship bias, technology bubble, The Great Moderation, The Wisdom of Crowds, transaction costs, tulip mania, uptick rule, Vanguard fund, vertical integration

But if inflation again rears its head, investors will do much better in stocks than in bonds. This page intentionally left blank 12 CHAPTER STOCKS AND THE BUSINESS CYCLE The stock market has predicted nine out of the last five recessions! PA U L S A M U E L S O N , 1 9 6 6 1 I’d love to be able to predict markets and anticipate recessions, but since that’s impossible, I’m as satisfied to search out profitable companies as Buffett is. P E T E R LY N C H , 1 9 8 9 2 A well-respected economist is about to address a large group of financial analysts, investment advisors, and stockbrokers. There is obvious concern in the audience.


The Trade Lifecycle: Behind the Scenes of the Trading Process (The Wiley Finance Series) by Robert P. Baker

asset-backed security, bank run, banking crisis, Basel III, Black-Scholes formula, book value, Brownian motion, business continuity plan, business logic, business process, collapse of Lehman Brothers, corporate governance, credit crunch, Credit Default Swap, diversification, financial engineering, fixed income, functional programming, global macro, hiring and firing, implied volatility, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, low interest rates, margin call, market clearing, millennium bug, place-making, prediction markets, proprietary trading, short selling, statistical model, stochastic process, the market place, the payments system, time value of money, too big to fail, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

As well as looking at the past, market risk needs a thorough understanding of current exposures and will generally use official data or possibly live data for this purpose. Credit risk control Credit risk control staff are interested in measuring exposure to counterparties’ default on payment. They are concerned with current and predicted market positions and will use data accordingly. Finance The finance department prepares and maintains the official books and records. It would normally use data approved by middle office. Legal The legal department is generally more concerned with the context and definition of data than the content.


pages: 369 words: 128,349

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing by Vijay Singal

3Com Palm IPO, Andrei Shleifer, AOL-Time Warner, asset allocation, book value, buy and hold, capital asset pricing model, correlation coefficient, cross-subsidies, currency risk, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, fixed income, index arbitrage, index fund, information asymmetry, information security, junk bonds, liberal capitalism, locking in a profit, Long Term Capital Management, loss aversion, low interest rates, margin call, market friction, market microstructure, mental accounting, merger arbitrage, Myron Scholes, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, short squeeze, survivorship bias, Tax Reform Act of 1986, transaction costs, uptick rule, Vanguard fund

If insiders can accurately predict how their company is likely to perform, then an aggregation of all insider recommendations should suggest whether the overall market or an industry is a buy, sell, or hold. The Vickers weekly insider report (go to vickers-stock.com) computes an “Insider Index” based on insider trading among all companies that is supposed to predict market direction. Similarly, Thomson provides its assessment of the market through its “Market Tearsheet” (go to insider.thomsonfn.com). The third source is WallStreetCity (go to www.wallstreetcity.com), which lists insider trading by industry. While these indices may be useful, the construction of the indices is critical.


pages: 457 words: 143,967

The Bank That Lived a Little: Barclays in the Age of the Very Free Market by Philip Augar

"Friedman doctrine" OR "shareholder theory", activist fund / activist shareholder / activist investor, Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, Bear Stearns, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Bonfire of the Vanities, bonus culture, book value, break the buck, business logic, call centre, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, family office, financial deregulation, financial innovation, fixed income, foreign exchange controls, Glass-Steagall Act, high net worth, hiring and firing, index card, index fund, interest rate derivative, light touch regulation, loadsamoney, Long Term Capital Management, long term incentive plan, low interest rates, Martin Wolf, money market fund, moral hazard, Nick Leeson, Northern Rock, offshore financial centre, old-boy network, out of africa, prediction markets, proprietary trading, quantitative easing, risk free rate, Ronald Reagan, shareholder value, short selling, Sloane Ranger, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, too big to fail, vertical integration, wikimedia commons, yield curve

Three weeks later there was more bad news. One of Wall Street’s most prestigious funds, Long Term Capital Management, which boasted a roster of smart investment bankers, Nobel Prize-winning economists and rocket-scientist traders among its principals, became insolvent. They used computer models to predict market movements and borrowed huge amounts of money to leverage their bets. Barclays and other banks had been falling over themselves to provide LTCM credit, taking a small fee while LTCM’s investors made huge returns. After a four-year winning streak, the Russian crisis prompted a sell-off in other emerging markets, the LTCM models failed and the hedge fund was bust.


pages: 496 words: 131,938

The Future Is Asian by Parag Khanna

3D printing, Admiral Zheng, affirmative action, Airbnb, Amazon Web Services, anti-communist, Asian financial crisis, asset-backed security, augmented reality, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Basel III, bike sharing, birth tourism , blockchain, Boycotts of Israel, Branko Milanovic, British Empire, call centre, capital controls, carbon footprint, cashless society, clean tech, clean water, cloud computing, colonial rule, commodity super cycle, computer vision, connected car, corporate governance, CRISPR, crony capitalism, cross-border payments, currency peg, death from overwork, deindustrialization, Deng Xiaoping, Didi Chuxing, Dissolution of the Soviet Union, Donald Trump, driverless car, dual-use technology, energy security, European colonialism, factory automation, failed state, fake news, falling living standards, family office, financial engineering, fixed income, flex fuel, gig economy, global reserve currency, global supply chain, Great Leap Forward, green transition, haute couture, haute cuisine, illegal immigration, impact investing, income inequality, industrial robot, informal economy, initial coin offering, Internet of things, karōshi / gwarosa / guolaosi, Kevin Kelly, Kickstarter, knowledge worker, light touch regulation, low cost airline, low skilled workers, Lyft, machine translation, Malacca Straits, Marc Benioff, Mark Zuckerberg, Masayoshi Son, megacity, megaproject, middle-income trap, Mikhail Gorbachev, money market fund, Monroe Doctrine, mortgage debt, natural language processing, Netflix Prize, new economy, off grid, oil shale / tar sands, open economy, Parag Khanna, payday loans, Pearl River Delta, prediction markets, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, reserve currency, ride hailing / ride sharing, Ronald Reagan, Salesforce, Scramble for Africa, self-driving car, Shenzhen special economic zone , Silicon Valley, smart cities, SoftBank, South China Sea, sovereign wealth fund, special economic zone, stem cell, Steve Jobs, Steven Pinker, supply-chain management, sustainable-tourism, synthetic biology, systems thinking, tech billionaire, tech worker, trade liberalization, trade route, transaction costs, Travis Kalanick, uber lyft, upwardly mobile, urban planning, Vision Fund, warehouse robotics, Washington Consensus, working-age population, Yom Kippur War

But as the political psychologist Philip Tetlock has demonstrated, full transparency over political deliberations can lead to decisions aimed at being popular rather than correct.10 Hence democracy must be supplemented by technocratic instruments that assess the long-term implications of decisions and offer correctives. Tetlock’s work also demonstrates the failure of experts to correctly predict a range of political and economic events. This is not a knock on technocracy. Governance is not about predictions but about decisions. Technocrats aren’t supposed to compete in prediction markets but listen to them, as well as to subject-matter experts and the public, and craft holistic policy. Indeed, there is ample evidence that Singapore’s ruling party, even though it faces little electoral competition, responds to citizens’ concerns and even reverses course on policies when necessary.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bear Stearns, Bernie Madoff, Black Swan, bond market vigilante , book value, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, commodity super cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, equity risk premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, global macro, Greenspan put, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, inverted yield curve, invisible hand, junk bonds, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, market microstructure, Minsky moment, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, proprietary trading, purchasing power parity, quantitative easing, random walk, Reminiscences of a Stock Operator, reserve currency, risk free rate, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, SoftBank, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, tail risk, The Great Moderation, Thomas Bayes, time value of money, too big to fail, Tragedy of the Commons, transaction costs, two and twenty, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

Then you post a poll saying everyone believes that “A” will happen. But if you change the 60 percent to 40 percent and ask people the same question, they will respond that “B” will happen. Changing the probability only 20 percent swung the “expected outcome” from 100 percent “A” to 100 percent “B.” This is how I see my role in terms of predicting market sentiment. I do not go around asking people how they are feeling, but I look for cases where a small change in fundamentals could cause a large change in how people perceive the fundamentals. There have been large swings in sentiment during the past few years. How do you stay ahead of these swings when they are driven by only small changes in fundamentals?


pages: 461 words: 128,421

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street by Justin Fox

"Friedman doctrine" OR "shareholder theory", Abraham Wald, activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, AOL-Time Warner, asset allocation, asset-backed security, bank run, beat the dealer, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, card file, Carl Icahn, Cass Sunstein, collateralized debt obligation, compensation consultant, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, democratizing finance, Dennis Tito, discovery of the americas, diversification, diversified portfolio, Dr. Strangelove, Edward Glaeser, Edward Thorp, endowment effect, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Glass-Steagall Act, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Bogle, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, market design, Michael Milken, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, power law, prediction markets, proprietary trading, prudent man rule, pushing on a string, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, rolodex, Ronald Reagan, seminal paper, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, Skinner box, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, stocks for the long run, tech worker, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, Two Sigma, Tyler Cowen, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

“Market expectations, therefore, have a certain necessary inaccuracy.” His concern was how much “objectionable inaccuracy” there might be, due to speculators overreacting to news or taking too long to digest it. Any sort of persistent errors on the part of speculators would lead to persistent, predictable market patterns: If it is possible under any given set of circumstances to predict future price changes and have the predictions fulfilled, it follows that the market expectations must have been defective; ideal market expectations would have taken full account of the information which permitted successful prediction of the price change.40 Working wrote this at a time when most economists still agreed with Keynes’s depiction of securities markets as a futile exercise in “anticipating what average opinion expects average opinion to be.”


Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

"World Economic Forum" Davos, Airbnb, Alan Greenspan, Albert Einstein, algorithmic management, algorithmic trading, Arthur D. Levinson, barriers to entry, behavioural economics, cloud computing, collaborative economy, commoditize, confounding variable, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, deep learning, demand response, Didi Chuxing, digital capitalism, disintermediation, disruptive innovation, double helix, Downton Abbey, driverless car, electricity market, Erik Brynjolfsson, Evgeny Morozov, experimental economics, Firefox, framing effect, Google Chrome, independent contractor, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, nowcasting, offshore financial centre, pattern recognition, power law, prediction markets, price discrimination, price elasticity of demand, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, search costs, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, sunk-cost fallacy, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

Federal Energy Regulatory Commission’s merger review policies were criticized for relying on data supplied by the regulated entities, rather than conducting its own independent fact gathering and analysis of market definition.42 The risk that sector regulators, even the most dedicated ones, may fail to understand and predict market dynamics, is real. Such failure will likely lead to a generalized approach that ultimately reduces welfare. In addition to the risks of imperfect information and regulatory capture, the government can undertake anticompetitive intervention because of weaker incentives to avoid mistakes than private actors who fully bear the costs of their mistakes, “political myopia,” and the lack of direct accountability to the public.43 Moreover, the road to perfect price regulation may also lead to a world of limited privacy, among other things.


pages: 517 words: 139,477

Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies by Jeremy Siegel

Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, backtesting, banking crisis, Bear Stearns, behavioural economics, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, break the buck, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, carried interest, central bank independence, cognitive dissonance, compound rate of return, computer age, computerized trading, corporate governance, correlation coefficient, Credit Default Swap, currency risk, Daniel Kahneman / Amos Tversky, Deng Xiaoping, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Financial Instability Hypothesis, fixed income, Flash crash, forward guidance, fundamental attribution error, Glass-Steagall Act, housing crisis, Hyman Minsky, implied volatility, income inequality, index arbitrage, index fund, indoor plumbing, inflation targeting, invention of the printing press, Isaac Newton, it's over 9,000, John Bogle, joint-stock company, London Interbank Offered Rate, Long Term Capital Management, loss aversion, machine readable, market bubble, mental accounting, Minsky moment, Money creation, money market fund, mortgage debt, Myron Scholes, new economy, Northern Rock, oil shock, passive investing, Paul Samuelson, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price stability, proprietary trading, purchasing power parity, quantitative easing, random walk, Richard Thaler, risk free rate, risk tolerance, risk/return, Robert Gordon, Robert Shiller, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, stocks for the long run, survivorship bias, technology bubble, The Great Moderation, the payments system, The Wisdom of Crowds, transaction costs, tulip mania, Tyler Cowen, Tyler Cowen: Great Stagnation, uptick rule, Vanguard fund

Fortunately for investors, central bankers around the world are committed to keeping inflation low, and they have largely succeeded. But if inflation again rears its head, investors will do much better in stocks than in bonds. 15 * * * Stocks and the Business Cycle The stock market has predicted nine out of the last five recessions. —PAUL SAMUELSON, 19661 I’d love to be able to predict markets and anticipate recessions, but since that’s impossible, I’m as satisfied to search out profitable companies as Buffett is. —PETER LYNCH, 19892 A well-respected economist is about to address a large group of financial analysts, investment advisors, and stockbrokers. There is obvious concern in the audience.


pages: 528 words: 146,459

Computer: A History of the Information Machine by Martin Campbell-Kelly, William Aspray, Nathan L. Ensmenger, Jeffrey R. Yost

Ada Lovelace, air freight, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple's 1984 Super Bowl advert, barriers to entry, Bill Gates: Altair 8800, Bletchley Park, borderless world, Buckminster Fuller, Build a better mousetrap, Byte Shop, card file, cashless society, Charles Babbage, cloud computing, combinatorial explosion, Compatible Time-Sharing System, computer age, Computer Lib, deskilling, don't be evil, Donald Davies, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Jenner, Evgeny Morozov, Fairchild Semiconductor, fault tolerance, Fellow of the Royal Society, financial independence, Frederick Winslow Taylor, game design, garden city movement, Gary Kildall, Grace Hopper, Herman Kahn, hockey-stick growth, Ian Bogost, industrial research laboratory, informal economy, interchangeable parts, invention of the wheel, Ivan Sutherland, Jacquard loom, Jeff Bezos, jimmy wales, John Markoff, John Perry Barlow, John von Neumann, Ken Thompson, Kickstarter, light touch regulation, linked data, machine readable, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Multics, natural language processing, Network effects, New Journalism, Norbert Wiener, Occupy movement, optical character recognition, packet switching, PageRank, PalmPilot, pattern recognition, Pierre-Simon Laplace, pirate software, popular electronics, prediction markets, pre–internet, QWERTY keyboard, RAND corporation, Robert X Cringely, Salesforce, scientific management, Silicon Valley, Silicon Valley startup, Steve Jobs, Steven Levy, Stewart Brand, Ted Nelson, the market place, Turing machine, Twitter Arab Spring, Vannevar Bush, vertical integration, Von Neumann architecture, Whole Earth Catalog, William Shockley: the traitorous eight, women in the workforce, young professional

At this point the traditional business-machine manufacturers, such as IBM, began to take an interest. THE IBM PC AND THE PC PLATFORM IBM was not, in fact, the giant that slept soundly during the personal-computer revolution. IBM had a sophisticated market research organization that attempted to predict market trends, and once the personal computer became clearly defined as a business machine in 1980, IBM reacted with surprising speed. The proposal that IBM should enter the personal-computer business came from William C. Lowe, a senior manager who headed the company’s “entry-level systems” division in Boca Raton, Florida.


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic bias, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, behavioural economics, Berlin Wall, Big Tech, Bill Duvall, bitcoin, Boeing 747, Charles Babbage, cognitive load, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, David Sedaris, delayed gratification, dematerialisation, diversification, Donald Knuth, Donald Shoup, double helix, Dutch auction, Elon Musk, exponential backoff, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, fulfillment center, Garrett Hardin, Geoffrey Hinton, George Akerlof, global supply chain, Google Chrome, heat death of the universe, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, Johannes Kepler, John Nash: game theory, John von Neumann, Kickstarter, knapsack problem, Lao Tzu, Leonard Kleinrock, level 1 cache, linear programming, martingale, multi-armed bandit, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, power law, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, Sam Altman, scientific management, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Stanford marshmallow experiment, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, Tragedy of the Commons, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game

Simple games like rock-paper-scissors may have equilibria visible at a glance, but in games of real-world complexity it’s now clear we cannot take for granted that the participants will be able to discover or reach the game’s equilibrium. This, in turn, means that the game’s designers can’t necessarily use the equilibrium to predict how the players will behave. The ramifications of this sobering result are profound: Nash equilibria have held a hallowed place within economic theory as a way to model and predict market behavior, but that place might not be deserved. As Papadimitriou explains, “If an equilibrium concept is not efficiently computable, much of its credibility as a prediction of the behavior of rational agents is lost.” MIT’s Scott Aaronson agrees. “In my opinion,” he says, “if the theorem that Nash equilibria exist is considered relevant to debates about (say) free markets versus government intervention, then the theorem that finding those equilibria is [intractable] should be considered relevant also.”


Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, data science, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Gregor Mendel, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, Large Hadron Collider, longitudinal study, machine readable, machine translation, Mars Rover, natural language processing, openstreetmap, Paradox of Choice, power law, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social bookmarking, social graph, SPARQL, sparse data, speech recognition, statistical model, supply-chain management, systematic bias, TED Talk, text mining, the long tail, Vernor Vinge, web application

As we’ve learned recently, it can be a mistake to assume discrete events (a homeowner defaulting on his mortgage, for example) are independent, and to build large edifices upon such assumptions (tradeable financial products sliced into tranches, for example) when they are not necessarily so. Prediction markets and group decision-making processes can work exceptionally well—in some cases, better than the estimates of a set of experts. They’ve been shown to break down, however, when information cascades and interdependencies enter the system (Bikhchandani et al. 1998). 9. Data Doesn’t Stand Alone In real-world decision-making, data comes in many forms.


Mastering Blockchain, Second Edition by Imran Bashir

3D printing, altcoin, augmented reality, autonomous vehicles, bitcoin, blockchain, business logic, business process, carbon footprint, centralized clearinghouse, cloud computing, connected car, cryptocurrency, data acquisition, Debian, disintermediation, disruptive innovation, distributed ledger, Dogecoin, domain-specific language, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, full stack developer, general-purpose programming language, gravity well, information security, initial coin offering, interest rate swap, Internet of things, litecoin, loose coupling, machine readable, MITM: man-in-the-middle, MVC pattern, Network effects, new economy, node package manager, Oculus Rift, peer-to-peer, platform as a service, prediction markets, QR code, RAND corporation, Real Time Gross Settlement, reversible computing, RFC: Request For Comment, RFID, ride hailing / ride sharing, Satoshi Nakamoto, seminal paper, single page application, smart cities, smart contracts, smart grid, smart meter, supply-chain management, transaction costs, Turing complete, Turing machine, Vitalik Buterin, web application, x509 certificate

Applications (DApps and DAOs) developed on Ethereum There are various implementations of DAOs and smart contracts in Ethereum, most notably, the DAO, which was recently misused due to a weakness in the code and required a hard fork for funds to be recovered that have been syphoned out by the attackers. The DAO was created to serve as a decentralized platform to collect and distribute investments. Augur is another DApp that has been implemented on Ethereum, which is a decentralized prediction market. Many other decentralized applications are listed on https://www.stateofthedapps.com/. Tools Various frameworks and tools have been developed to support decentralized application development such as Truffle, MetaMask, Ganache, TestRPC and many more. We will talk about these in Chapter 13, Development Tools and Frameworks.


pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

"World Economic Forum" Davos, 23andMe, 4chan, A Declaration of the Independence of Cyberspace, Aaron Swartz, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Big Tech, Brian Krebs, California gold rush, Californian Ideology, call centre, cloud computing, cognitive dissonance, commoditize, company town, context collapse, correlation does not imply causation, Credit Default Swap, crowdsourcing, data science, deep learning, digital capitalism, disinformation, don't be evil, driverless car, drone strike, Edward Snowden, Evgeny Morozov, fake it until you make it, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, Higgs boson, hive mind, Ian Bogost, income inequality, independent contractor, informal economy, information retrieval, Internet of things, Jacob Silverman, Jaron Lanier, jimmy wales, John Perry Barlow, Kevin Kelly, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, Larry Ellison, late capitalism, Laura Poitras, license plate recognition, life extension, lifelogging, lock screen, Lyft, machine readable, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, off-the-grid, optical character recognition, payday loans, Peter Thiel, planned obsolescence, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, real-name policy, recommendation engine, rent control, rent stabilization, RFID, ride hailing / ride sharing, Salesforce, self-driving car, sentiment analysis, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Snapchat, social bookmarking, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, systems thinking, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, yottabyte, you are the product, Zipcar

Like viral media, there may be a smattering of success stories, people who rise above the herd and manage to master the system, but the most productive online laborers usually manage to make only a few dollars per hour. Premise—a company specializing in what it calls “hyperdata,” using rapidly collected photos of goods, particularly in grocery stores, to predict market conditions—pays its photographers 8 to 10 cents per snapshot. Premise then passes its analysis on to hedge funds and major conglomerates, who pay four-or five-figure monthly subscription fees. Some industry leaders argue that this kind of labor opens up new possibilities of work that otherwise wouldn’t exist without distributed micro-work and smartphone-connected laborers.


pages: 391 words: 22,799

To Serve God and Wal-Mart: The Making of Christian Free Enterprise by Bethany Moreton

affirmative action, American Legislative Exchange Council, anti-communist, Berlin Wall, big-box store, Bretton Woods, Buckminster Fuller, collective bargaining, company town, corporate personhood, creative destruction, deindustrialization, desegregation, Donald Trump, emotional labour, estate planning, eternal september, Fall of the Berlin Wall, Frederick Winslow Taylor, George Gilder, global village, Great Leap Forward, informal economy, invisible hand, liberation theology, longitudinal study, market fundamentalism, Mont Pelerin Society, mortgage tax deduction, Naomi Klein, new economy, post-industrial society, postindustrial economy, prediction markets, price anchoring, prosperity theology / prosperity gospel / gospel of success, Ralph Nader, RFID, road to serfdom, Ronald Reagan, scientific management, Silicon Valley, Stewart Brand, strikebreaker, The Wealth of Nations by Adam Smith, union organizing, walkable city, Washington Consensus, white flight, Whole Earth Catalog, work culture , Works Progress Administration

The result was an irresistible target for the Arkansans: an audience of slightly bewildered city folk, struggling to comprehend the company’s magic. With encouragement from Walton, Senior Vice President Ron Loveless elaborated on one of management’s typical in-Â�house gags 6 OUR FATHERS’ AMER I CA and presented it to the attentive crowd. “People often ask us how we predict market demand for discount merchandise,” Loveless began, and you’ve heard a lot of numbers today. But there is more to it€than that. We raise a good many chickens in Northwest Arkansas, and we’ve come to depend on them for what we call the Loveless Economic Indicator Report. You see, when times are good, you find plenty of dead chickens by the side of the road, ones that have fallen off the trucks.


The Volatility Smile by Emanuel Derman,Michael B.Miller

Albert Einstein, Asian financial crisis, Benoit Mandelbrot, Black Monday: stock market crash in 1987, book value, Brownian motion, capital asset pricing model, collateralized debt obligation, continuous integration, Credit Default Swap, credit default swaps / collateralized debt obligations, discrete time, diversified portfolio, dividend-yielding stocks, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, implied volatility, incomplete markets, law of one price, London Whale, mandelbrot fractal, market bubble, market friction, Myron Scholes, prediction markets, quantitative trading / quantitative finance, risk tolerance, riskless arbitrage, Sharpe ratio, statistical arbitrage, stochastic process, stochastic volatility, transaction costs, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

Thus, some of the apparent correlation in Figure 8.9 would occur even if the smile stayed completely stationary as the market dropped. How much of the correlation is true comovement and not merely a consequence of a negative skew? We will see later in the book that different models produce different predictions. Market participants often talk about how “volatility changed.” One must be very precise in speaking about volatility changes because there are so many different kinds of volatility: realized volatility, at-the-money volatility, and the implied volatility of a particular strike. In option markets, the most commonly referred to volatility is current at-the-money implied volatility.


pages: 532 words: 141,574

Bleeding Edge: A Novel by Thomas Pynchon

addicted to oil, AltaVista, anti-communist, Anton Chekhov, Bernie Madoff, big-box store, Burning Man, carried interest, deal flow, Donald Trump, double entry bookkeeping, East Village, eternal september, false flag, fixed-gear, gentrification, Hacker Ethic, index card, invisible hand, jitney, Larry Ellison, late capitalism, margin call, messenger bag, Network effects, Ponzi scheme, prediction markets, pre–internet, QWERTY keyboard, RAND corporation, rent control, rolodex, Ronald Reagan, Sand Hill Road, Silicon Valley, telemarketer, Y2K

The times we shorted Amazon, got out of Lucent when it went to $70 a share, remember? It wasn’t me that ever ‘knew’ anything. But something did. Sudden couple extra lines of brain code, who knows. I just followed along.” “But then . . . if it was that same weird talent that kept you safe . . .” “How could it be? How could predicting market behavior be the same as predicting a terrible disaster?” “If the two were different forms of the same thing.” “Way too anticapitalist for me, babe.” Later he reflects, “You always had me figured for some kind of idiot savant, you were the one with the street smarts, the wised-up practical one, and I was just some stiff with a gift, who didn’t deserve to be so lucky.”


pages: 512 words: 162,977

New Market Wizards: Conversations With America's Top Traders by Jack D. Schwager

backtesting, beat the dealer, Benoit Mandelbrot, Berlin Wall, Black-Scholes formula, book value, butterfly effect, buy and hold, commodity trading advisor, computerized trading, currency risk, Edward Thorp, Elliott wave, fixed income, full employment, implied volatility, interest rate swap, Louis Bachelier, margin call, market clearing, market fundamentalism, Market Wizards by Jack D. Schwager, money market fund, paper trading, pattern recognition, placebo effect, prediction markets, proprietary trading, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, risk tolerance, risk/return, Saturday Night Live, Sharpe ratio, the map is not the territory, transaction costs, uptick rule, War on Poverty

If you ever find yourself tempted to seek out someone else’s opinion on a trade, that’s usually a sure sign that you should get out of your position. What are your goals? There’s no better satisfaction than playing a piece well, whether the instrument is a piano or the markets. I measure my progress not in dollars but in my skill in predicting market patterns—that is, in how close I can come to pinpointing my entries and exits to the market turns. I believe that I can go into any market with just a quote machine and out-trade 98 percent of the other traders. Over the next ten years, I would like to significantly step up my trading size. I really believe that I can become one of the best traders around. 310 / The New Market Wizard Certainly one of the primary common characteristics I have found among the great traders is an almost compelling sense of confidence in their ability to succeed.


pages: 662 words: 180,546

Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown by Philip Mirowski

"there is no alternative" (TINA), Adam Curtis, Alan Greenspan, Alvin Roth, An Inconvenient Truth, Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, bond market vigilante , bread and circuses, Bretton Woods, Brownian motion, business cycle, capital controls, carbon credits, Carmen Reinhart, Cass Sunstein, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, constrained optimization, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, dark matter, David Brooks, David Graeber, debt deflation, deindustrialization, democratizing finance, disinformation, do-ocracy, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, Flash crash, full employment, George Akerlof, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Greenspan put, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, information asymmetry, invisible hand, Jean Tirole, joint-stock company, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kickstarter, knowledge economy, l'esprit de l'escalier, labor-force participation, liberal capitalism, liquidity trap, loose coupling, manufacturing employment, market clearing, market design, market fundamentalism, Martin Wolf, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Naomi Klein, Nash equilibrium, night-watchman state, Northern Rock, Occupy movement, offshore financial centre, oil shock, Pareto efficiency, Paul Samuelson, payday loans, Philip Mirowski, Phillips curve, Ponzi scheme, Post-Keynesian economics, precariat, prediction markets, price mechanism, profit motive, public intellectual, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, savings glut, school choice, sealed-bid auction, search costs, Silicon Valley, South Sea Bubble, Steven Levy, subprime mortgage crisis, tail risk, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, tontine, too big to fail, transaction costs, Tyler Cowen, vertical integration, Vilfredo Pareto, War on Poverty, Washington Consensus, We are the 99%, working poor

See also Chwieroth, Capital Ideas. 100 Hayek, Studies in Philosophy, Politics and Economics, p. 172. 101 See the papers and data available for download at http://elsa.berkeley.edu/~saez/. 102 Rajan, Fault Lines. 103 See Van Horn and Mirowski, “The Rise of the Chicago School and the Birth of Neoliberalism.” 104 Nace, Gangs of America. 105 Jensen and Meckling, “Theory of the Firm.” 106 Nik-Khah, “A Tale of Two Auctions.” 107 On the ill-fated DARPA “policy analysis market” project, see www.sfgate.com/cgi-bin/article.cgi?file=/c/a/2003/07/29/MN126930.DTL (accessed December 2, 2006) and Justin Wolfers and Eric Zitzowitz, “Prediction Markets in Theory and Practice,” www.dartmouth.edu/~ericz/palgrave.pdf. 108 For unabashed examples of this neoliberal argument, see Litan, “In Defense of Much, but Not All, Financial Innovation” and The Derivatives Dealer’s Club; Shiller, Finance and the Good Society. A more skeptical summary is Engelen, et al., After the Great Complacence.


pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

"World Economic Forum" Davos, 23andMe, AltaVista, Andy Rubin, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, Bill Atkinson, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dutch auction, El Camino Real, Evgeny Morozov, fault tolerance, Firefox, General Magic , Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, high-speed rail, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Ken Thompson, Kevin Kelly, Kickstarter, large language model, machine translation, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, PalmPilot, Paul Buchheit, Potemkin village, prediction markets, Project Xanadu, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, Sheryl Sandberg, Silicon Valley, SimCity, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, Ted Nelson, telemarketer, The future is already here, the long tail, trade route, traveling salesman, turn-by-turn navigation, undersea cable, Vannevar Bush, web application, WikiLeaks, Y Combinator

After a number of involuntary exchanges, people either stopped checking or learned to hide their peeking more effectively. But Googlers were affected by stock ownership. (They were, after all, human.) Bo Cowgill, a Google statistician, did a series of studies of his colleagues’ behavior, based on their participation in a “prediction market,” a setup that allowed them to make bets on the success of internal projects. He discovered that “daily stock price movements affect the mood, effort level and decision-making of employees.” As you’d expect, increases in stock performance made people happier and more optimistic—but they also led them to regard innovative ideas more warily, indicating that as Googlers became richer, they became more conservative.


pages: 781 words: 226,928

Commodore: A Company on the Edge by Brian Bagnall

Apple II, belly landing, Bill Gates: Altair 8800, Byte Shop, Claude Shannon: information theory, computer age, Computer Lib, Dennis Ritchie, Douglas Engelbart, Douglas Engelbart, Firefox, Ford Model T, game design, Gary Kildall, Great Leap Forward, index card, inventory management, Isaac Newton, Ken Thompson, low skilled workers, Menlo Park, packet switching, pink-collar, popular electronics, prediction markets, pre–internet, QWERTY keyboard, Robert Metcalfe, Robert X Cringely, Silicon Valley, special economic zone, Steve Jobs, Steve Wozniak, systems thinking, Ted Nelson, vertical integration

“I think it might have been, ‘Is it ready enough to show?’ And he made that decision there.” The nervous engineers displayed the result of almost two months of compressed labor. “We told him what it was, how simple it was, and what it could cost,” recalls Russell. “He said, ‘Put it on the floor.’” As Winterble predicted, marketing was not happy to learn of the project so late in development. “When these guys found out about it, and found out that they were not involved in it, then right away you can imagine: it hit the fan,” says Winterble. “It was turmoil.” With no advanced warning, Kit Spencer had to work non-stop to prepare print material for the prototype.


pages: 843 words: 223,858

The Rise of the Network Society by Manuel Castells

air traffic controllers' union, Alan Greenspan, Apple II, Asian financial crisis, barriers to entry, Big bang: deregulation of the City of London, Bob Noyce, borderless world, British Empire, business cycle, capital controls, classic study, complexity theory, computer age, Computer Lib, computerized trading, content marketing, creative destruction, Credit Default Swap, declining real wages, deindustrialization, delayed gratification, dematerialisation, deskilling, digital capitalism, digital divide, disintermediation, double helix, Douglas Engelbart, Douglas Engelbart, edge city, experimental subject, export processing zone, Fairchild Semiconductor, financial deregulation, financial independence, floating exchange rates, future of work, gentrification, global village, Gunnar Myrdal, Hacker Ethic, hiring and firing, Howard Rheingold, illegal immigration, income inequality, independent contractor, Induced demand, industrial robot, informal economy, information retrieval, intermodal, invention of the steam engine, invention of the telephone, inventory management, Ivan Sutherland, James Watt: steam engine, job automation, job-hopping, John Markoff, John Perry Barlow, Kanban, knowledge economy, knowledge worker, labor-force participation, laissez-faire capitalism, Leonard Kleinrock, longitudinal study, low skilled workers, manufacturing employment, Marc Andreessen, Marshall McLuhan, means of production, megacity, Menlo Park, military-industrial complex, moral panic, new economy, New Urbanism, offshore financial centre, oil shock, open economy, packet switching, Pearl River Delta, peer-to-peer, planetary scale, popular capitalism, popular electronics, post-Fordism, post-industrial society, Post-Keynesian economics, postindustrial economy, prediction markets, Productivity paradox, profit maximization, purchasing power parity, RAND corporation, Recombinant DNA, Robert Gordon, Robert Metcalfe, Robert Solow, seminal paper, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social software, South China Sea, South of Market, San Francisco, special economic zone, spinning jenny, statistical model, Steve Jobs, Steve Wozniak, Strategic Defense Initiative, tacit knowledge, technological determinism, Ted Nelson, the built environment, the medium is the message, the new new thing, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, total factor productivity, trade liberalization, transaction costs, urban renewal, urban sprawl, vertical integration, work culture , zero-sum game

Or, to put it in other words, it is based on the assumption of the “five zeros”: zero defect in the parts; zero mischief in the machines; zero inventory; zero delay; zero paperwork. Such performances can only be predicated on the basis of an absence of work stoppages and total control over labor, on entirely reliable suppliers, and on adequately predicted markets. “Toyotism” is a management system designed to reduce uncertainty rather than to encourage adaptability. The flexibility is in the process, not in the product. Thus, some analysts have suggested that it could be considered as an extension of “Fordism,”26 keeping the same principles of mass production, yet organizing the production process on the basis of human initiative and feedback capacity to eliminate waste (of time, work, and resources) while maintaining the characteristics of output close to the business plan.


pages: 745 words: 207,187

Accessory to War: The Unspoken Alliance Between Astrophysics and the Military by Neil Degrasse Tyson, Avis Lang

active measures, Admiral Zheng, airport security, anti-communist, Apollo 11, Arthur Eddington, Benoit Mandelbrot, Berlin Wall, British Empire, Buckminster Fuller, Carrington event, Charles Lindbergh, collapse of Lehman Brothers, Colonization of Mars, commoditize, corporate governance, cosmic microwave background, credit crunch, cuban missile crisis, dark matter, Dava Sobel, disinformation, Donald Trump, Doomsday Clock, Dr. Strangelove, dual-use technology, Eddington experiment, Edward Snowden, energy security, Eratosthenes, European colonialism, fake news, Fellow of the Royal Society, Ford Model T, global value chain, Google Earth, GPS: selective availability, Great Leap Forward, Herman Kahn, Higgs boson, invention of movable type, invention of the printing press, invention of the telescope, Isaac Newton, James Webb Space Telescope, Johannes Kepler, John Harrison: Longitude, Karl Jansky, Kuiper Belt, Large Hadron Collider, Late Heavy Bombardment, Laura Poitras, Lewis Mumford, lone genius, low earth orbit, mandelbrot fractal, Maui Hawaii, Mercator projection, Mikhail Gorbachev, military-industrial complex, mutually assured destruction, Neil Armstrong, New Journalism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, operation paperclip, pattern recognition, Pierre-Simon Laplace, precision agriculture, prediction markets, profit motive, Project Plowshare, purchasing power parity, quantum entanglement, RAND corporation, Ronald Reagan, Search for Extraterrestrial Intelligence, skunkworks, South China Sea, space junk, Stephen Hawking, Strategic Defense Initiative, subprime mortgage crisis, the long tail, time dilation, trade route, War on Poverty, wikimedia commons, zero-sum game

Kleinfield, “Seeing Dollar Signs in Searching the Stars,” New York Times, May 15, 1988; Gary Weiss, “When Scorpio Rises, Stocks Will Fall,” Business Week, June 14, 1993, 106; Anne Matthews, “Markets Rise and Fall, but He’s Always Looking Up,” New York Times, Mar. 12, 1995; Reid Kanaley, “Astrological Web Sites Predict Market Movements,” Philadelphia Inquirer, Oct. 15, 1999; “Investrend Co-Sponsors Astrologers Fund Triple Gold Investment Conference February 1,” Financial Times Information, Jan. 30, 2006; David Roeder, “Some Large-cap Deals Hide in Plain Sight,” Chicago Sun Times, Apr. 30, 2006. 39.Ilia D. Dichev and Troy D.


pages: 1,737 words: 491,616

Rationality: From AI to Zombies by Eliezer Yudkowsky

Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, antiwork, Arthur Eddington, artificial general intelligence, availability heuristic, backpropagation, Bayesian statistics, behavioural economics, Berlin Wall, Boeing 747, Build a better mousetrap, Cass Sunstein, cellular automata, Charles Babbage, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, disinformation, Douglas Hofstadter, Drosophila, Eddington experiment, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Great Leap Forward, Gödel, Escher, Bach, Hacker News, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Large Hadron Collider, Long Term Capital Management, Louis Pasteur, mental accounting, meta-analysis, mirror neurons, money market fund, Monty Hall problem, Nash equilibrium, Necker cube, Nick Bostrom, NP-complete, One Laptop per Child (OLPC), P = NP, paperclip maximiser, pattern recognition, Paul Graham, peak-end rule, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, scientific worldview, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, SpaceShipOne, speech recognition, statistical model, Steve Jurvetson, Steven Pinker, strong AI, sunk-cost fallacy, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, Tyler Cowen, ultimatum game, X Prize, Y Combinator, zero-sum game

Lonely dissent, I called it. True dissent doesn’t feel like going to school wearing black; it feels like going to school wearing a clown suit. That’s what it takes to be the lone voice who says, “If you really think you know who’s going to win the election, why aren’t you picking up the free money on the Intrade prediction market?” while all the people around you are thinking, “It is good to be an individual and form your own opinions, the shoe commercials told me so.” Maybe in some other world, some alternate Everett branch with a saner human population, things would be different . . . but in this world, I’ve never seen anyone begin to grow as a rationalist until they make a deep emotional break with the wisdom of their pack.

Do not rely on being able to successfully think high-minded thoughts unless experimentation is so costly or dangerous that you have no other choice. But sometimes experiments are costly, and sometimes we prefer to get there first . . . so you might consider trying to train yourself in reasoning on scanty evidence, preferably in cases where you will later find out if you were right or wrong. Trying to beat low-capitalization prediction markets might make for good training in this?—though that is only speculation. As of now, at least, reasoning based on scanty evidence is something that modern-day science cannot reliably train modern-day scientists to do at all. Which may perhaps have something to do with, oh, I don’t know, not even trying?


pages: 798 words: 240,182

The Transhumanist Reader by Max More, Natasha Vita-More

"World Economic Forum" Davos, 23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, Computing Machinery and Intelligence, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Douglas Engelbart, Drosophila, en.wikipedia.org, endogenous growth, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, Future Shock, game design, germ theory of disease, Hans Moravec, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, Louis Pasteur, Menlo Park, meta-analysis, moral hazard, Network effects, Nick Bostrom, Norbert Wiener, pattern recognition, Pepto Bismol, phenotype, positional goods, power law, precautionary principle, prediction markets, presumed consent, Project Xanadu, public intellectual, radical life extension, Ray Kurzweil, reversible computing, RFID, Ronald Reagan, scientific worldview, silicon-based life, Singularitarianism, social intelligence, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, synthetic biology, systems thinking, technological determinism, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, VTOL, Whole Earth Review, women in the workforce, zero-sum game

Robin Hanson, PhD, is Associate Professor of Economics, George Mason University. He authored “Meet the New Conflict, Same as the Old Conflict” (Journal of Consciousness Studies 19, 2012); “Enhancing our Truth Orientation” (Human Enhancement, Oxford University Press, 2009); and “Insider Trading and Prediction Markets” (Journal of Law, Economics, and Policy 4, 2008). Patrick D. Hopkins, PhD, is Associate Professor, Philosophy and Gender Studies, Millsaps College. He authored Sex/Machine: Readings in Culture, Gender, and Technology (Indiana University Press, 1999); and co-authored with Larry May et al.


pages: 1,034 words: 241,773

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker

3D printing, Abraham Maslow, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, Alignment Problem, An Inconvenient Truth, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, biodiversity loss, Black Swan, Bonfire of the Vanities, Brexit referendum, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, Charlie Hebdo massacre, classic study, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, confounding variable, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, decarbonisation, degrowth, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, Eddington experiment, Edward Jenner, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, fake news, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, Garrett Hardin, germ theory of disease, Gini coefficient, Great Leap Forward, Hacker Conference 1984, Hans Rosling, hedonic treadmill, helicopter parent, Herbert Marcuse, Herman Kahn, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, launch on warning, life extension, long peace, longitudinal study, Louis Pasteur, Mahbub ul Haq, Martin Wolf, mass incarceration, meta-analysis, Michael Shellenberger, microaggression, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, negative emissions, Nelson Mandela, New Journalism, Norman Mailer, nuclear taboo, nuclear winter, obamacare, ocean acidification, Oklahoma City bombing, open economy, opioid epidemic / opioid crisis, paperclip maximiser, Paris climate accords, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, post-truth, power law, precautionary principle, precision agriculture, prediction markets, public intellectual, purchasing power parity, radical life extension, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, Social Justice Warrior, sovereign wealth fund, sparse data, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, synthetic biology, tech billionaire, technological determinism, technological singularity, Ted Kaczynski, Ted Nordhaus, TED Talk, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, Tragedy of the Commons, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, W. E. B. Du Bois, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

Tetlock and the psychologist Barbara Mellers held a rematch between 2011 and 2015 in which they recruited several thousand contestants to take part in a forecasting tournament held by the Intelligence Advanced Research Projects Activity (the research organization of the federation of American intelligence agencies). Once again there was plenty of dart-throwing, but in both tournaments the couple could pick out “superforecasters” who performed not just better than chimps and pundits, but better than professional intelligence officers with access to classified information, better than prediction markets, and not too far from the theoretical maximum. How can we explain this apparent clairvoyance? (For a year, that is—accuracy declines with distance into the future, and it falls to the level of chance around five years out.) The answers are clear and profound. The forecasters who did the worst were the ones with Big Ideas—left-wing or right-wing, optimistic or pessimistic—which they held with an inspiring (but misguided) confidence: As ideologically diverse as they were, they were united by the fact that their thinking was so ideological.