price discovery process

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pages: 318 words: 87,570

Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio by Sal Arnuk, Joseph Saluzzi

algorithmic trading, automated trading system, Bernie Madoff, buttonwood tree, buy and hold, commoditize, computerized trading, corporate governance, cuban missile crisis, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, Mark Zuckerberg, market fragmentation, Ponzi scheme, price discovery process, price mechanism, price stability, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, stocks for the long run, stocks for the long term, transaction costs, two-sided market, zero-sum game

It was not due to small supply and rabid demand. One algorithm was pricing the book at 1.27 times the price of the other algorithm, which in turn would revise its price to 0.998 times the price of the first algorithm, creating a positive feedback loop. Momentum ignition is extremely damaging to long-term investors. It does not cost pennies; it costs them quarters, and it ferociously distorts the price discovery process. How the World Began to Learn About HFT While HFT has been steadily expanding since the millennium and exponentially since the implementation of the SEC’s Reg NMS in 2007, it stayed out of the mainstream media until mid-2009. Over the July 4 weekend, Sergey Aleynikov was arrested by the FBI at Newark Airport for stealing code from his prior employer, Goldman Sachs, and trying to bring it to his new job as an HFT programmer at Teza Technologies, where he was set to triple his $400,000 salary.

The proposals called for dark pool indications of interest to be treated like regular quotes and therefore required to be visible to all investors; real time disclosure of the identity of the dark pool that executed a trade; and any dark pool that traded more than 0.25% of a stock had to display those orders in the public quote.8, 9 Ironically, Reg ATS in 1998 was supposed to force more orders into the public quote to aid in the price discovery process. However, it resulted in almost one-third of orders being executed away from the public quote and in dark pools. With the flash order and dark pool proposals, it appeared that the SEC was serious about increasing transparency. The SEC received hundreds of comment letters, most by industry participants who wanted to maintain the status quo. Unfortunately, even though proposed in 2009, the SEC has yet to approve any parts of either proposal.

Investors who thought they were protecting themselves with the prudent use of stop orders were left with fills that were far away from the closing price. Today’s severe market drop should never have happened. The U.S. equity market had been hailed as the best, most liquid market in the world. The market action of May 6 has demonstrated that our equity market has major systemic risks built into it. There was a time today when folks didn’t know the true price and value of a stock. The price discovery process ceased to exist. High frequency firms have always insisted that their mini-scalping activities stabilized markets and provided liquidity, and on May 6 they just shut down. Money began pouring out of the equity market, as nervous investors lost confidence. The SEC responded almost five months later, issuing a report titled “Findings Regarding Market Events of May 6, 2010.” The report placed a good piece of blame on one midwestern money manager.

pages: 273 words: 72,024

Bitcoin for the Befuddled by Conrad Barski

Airbnb, AltaVista, altcoin, bitcoin, blockchain, buttonwood tree, cryptocurrency, Debian,, Ethereum, ethereum blockchain, fiat currency, Isaac Newton, MITM: man-in-the-middle, money: store of value / unit of account / medium of exchange, Network effects, node package manager, p-value, peer-to-peer, price discovery process, QR code, Satoshi Nakamoto, self-driving car, SETI@home, software as a service, the payments system, Yogi Berra

Currently, Bitcoin has the largest adoption of any cryptocurrency, so newer ones would need to have easily distinguishable advantages over Bitcoin to overcome its network advantage. But how does volatility factor in? From an economics standpoint, any asset that becomes newly available to an open market needs to first undergo a price discovery process. This was part of the reason for the Internet bubble in 2000: People simply didn’t know the value of the stocks of eBay, Yahoo!, and other tech companies because similar companies had not existed in the past. Eventually, as people became more familiar with Internet-focused corporations, it became clearer how to reasonably assign a price to each company’s stock. Bitcoin has been undergoing a similar price discovery process, which is still in its very early stages: The price of a bitcoin has been swinging wildly up and down since the currency’s inception. As more and more users have started to use it, however, the volatility has modestly decreased (i.e., the swings, in relative terms, have become less violent).

program, 217–218, 220–222 hello-money starter project creating, 228–229 declarations, 231 hook for detecting money arrival, 234 running and testing, 235–236 writing code, 230–235 hierarchical deterministic wallets, 190 Hill, Austin, 120 history of Bitcoin, 112–116 homebrew (command-line tool), 219 hosted wallets online services, 36 vs. personal wallets, 34–35 hot storage, 47 vs. cold storage, 33–34 hot wallets, personal, 37–38 human-readable Bitcoin addresses, 10n hybrid wallets, 187 I illegal activity, Bitcoin and, 124 impedance mismatch, 57 importing private key, 17, 39, 193, 194–195, 237 installing SPV wallets vs. full wallets, 193 integer factorization, 131 Internet bubble, 120 InterruptedException exception type, 239 irreversibility, of transactions, 25–26, 56 superiority of, 57 J Java, 226 initializing objects, 231–233 installing, 226–227 class, 231 Java JDK (Java Development Kit), 226 java.matho.BigInteger class, 231 JavaScript, 213–223 preparing machine for, 218–219 writing Bitcoin program in, 217–218 jelly-filled donut incident, 141–156 JSON-RPC API (JavaScript Object Notation - Remote Protocol Call), 222 limitations of writing Bitcoin programs using, 223 JSON-RPC protocol, 214 K Kaminsky, Dan, 118 Keynesian economics, 126 Kienzle, Jörg, 110–111 Koblitz curve, 151 Kraken, 64 Krugman, Paul, 117 L Landauer limit, 157 laptops, private keys on, 44 ledger, 11 length extension, 171n liability, for stolen bitcoins, 34 lightweight wallets, 192 limit orders, 66 Linux installing Git, 227 installing Maven, 227 OpenJDK version of Java, 227 setting up Bitcoin Core server, 219 live Bitcoin exchanges, 71, 67, 68 escrow service, 70 M Mac OS installing Git, 227 installing Maven, 227 setting up Bitcoin Core server, 219 man-in-the-middle attacks, 216 market orders, 65–66 MasterCard, 112 master private key, 188 master public key, 188 generating Bitcoin address with, 190 Maven empty starter project created with, 228 installing, 227 mBTC (millibitcoins), 9 MD5 (message digest algorithm), 132 meeting places, for Bitcoin transactions, 68 MemoryBlockStore function (bitcoinJ), 237 merchant services, 214 Merkle trees, 192 mesh networks, 169 message digest algorithm (MD5), 132 microbitcoins (µBTC), 9 middleman, buying bitcoins from, 52–57 Miller-Rabin primality test, 90 millibitcoins (mBTC), 9 mining, 5, 20, 26–27, 96, 99, 161–180 in 2030, 201–202 decentralization of, 179–180 difficulty of, 173 distributing new currency with, 167–168 hardware, 174–175 2030 requirements, 202 energy efficiency of, 178 profitability threshold curves for comparing, 179 need for, 162–168 nodes, 170 pooled, 175–176 practicality, 50 preventing attacks with, 166–167 process for, 168–176 for profit, 176–177 proof-of-work in, 138–139 solving a block, 171 modular arithmetic, 131n “m of n” private key, 42 money laundering, 112–113 Moore’s law, 179n Moxie Jean, 67 Multibit, 38 multi-signature addresses, and fragmented private keys, 41–42 multi-signature transactions, 57, 69–70 mvn install command, 230 My Wallet Service, 37 N Nakamoto, Satoshi, 3, 110, 211 identity, 113 last comment, 114 white paper on Bitcoin, 112 network effect, 120 NetworkParameters structure, 232, 13 newly minted bitcoins, 26–27 Newton, Isaac, Principia, 210–211 node-bitcoin, installing, 218 Node.js library, 217, 221 installing, 218 Node Package Manager, 218 nodes broadcast only, 169 full, 191 relay, 170 nominal deflation, 126 nonprofit organizations, accepting bitcoins, 18 NXT, 125 O off-chain transactions, 201 offline transaction signing, 40–41 onCoinsReceived function, 234–235 online wallet services hosted, 36 personal, 34, 37 Oracle Corporation, 226 orders, placing to buy bitcoins, 65 order of curve, elliptic curve cryptography, 152–153 orphaned blocks, 24–25 P paper money, color copiers as threat, 110 paper wallets, 39 encrypted, 39–40 passwords, 14, 40 for brain wallet, 45 function of, 40 loss of, 37 Peercoin, 125 PeerGroup object, 233–234, 240 peer-to-peer architecture, 119 pegging, 120 pending transaction, 18 Perrig, Adrian, 110–111 personal wallets vs. hosted wallet, 34–35 hot storage, 37–38 online services, 37 person-to-person bitcoin purchases, 52, 67–71 point multiplication, 150, 158–159 point-of-sale terminals, watch-only wallet for, 187 polling, Bitcoin programming, 223 pom.xml file, 229, 236–237 pooled mining, 175–176 portability, of currency, 117 Preneel, Bart, 140 price discovery process, 120 privacy, 11n and criminals, 124 multiple addresses and, 12 private currencies, 2 private key, 11–12, 150 compromise of, 41 extra protection for, 139 fragmented, and multi-signature addresses, 41–42 generating, 37 importing, 237 master, 188 memorizing, 45 parable on, 141–145 reversing function of, 136 security for, 39, 186 signing transaction with, 156 SPV wallets vs. full wallets, 194 storing, 33 profit, mining for, 176–177 programming languages, for Bitcoin network connection, 225–226 proof-of-stake, 125 proof-of-work, 125, 166 and blockchain, 165 in mining, 138–139 protecting bitcoins, 61.

pages: 733 words: 179,391

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

"Robert Solow", 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, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, 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 Shiller, Sam Peltzman, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, survivorship 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, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

Every economic transaction has a buyer and a seller, each trying to come to a mutually satisfying agreement, Jevons’s “the double coincidence of wants.” Economists call this negotiation the price discovery process, as we saw in chapter 1 in the cobweb model of the hog cycle. However, this negotiation doesn’t always conclude with a consummated trade and a price. If a seller refuses to lower the asking price to a level a buyer wishes to pay, no transaction will take place. That potato chip shaped like Jay Leno’s head, currently offered on eBay for one hundred dollars, may never get sold if the seller is unwilling to lower the price. This might be a rational decision on the seller’s part. On the other hand, it might reflect a lack of awareness of what a buyer is willing to pay. The price discovery process in a well-functioning market requires its participants to engage in a certain degree of cause-and-effect reasoning.

The “cobweb” model of the hog cycle with oscillations that eventually converge to the equilibrium (P*, Q*). Start at Q 0, with expected price P0; actual price is P1, which yields supply Q1; Q1 yields actual price P2, and so on; the spiral continues until supply equals demand at (P*, Q*). the results of two recent German-language papers written separately by an American and an Italian, Henry Schultz and Umberto Ricci. Schultz and Ricci had independently examined what might happen if the price discovery process along the supply and demand curves wasn’t smooth or instantaneous, but if it instead took place in discrete lumps of time, like the turns in a game. To their surprise, Schultz and Ricci discovered that under those conditions, prices tended to move in cycles around the equilibrium point. In some cases, prices even diverged from the sweet spot of equilibrium entirely. Kaldor called this the “cobweb” theorem, due to the resemblance of the resulting supply and demand graphs to a spider’s web (see figure 1.3).

However, when we look at the details of their extraordinary track records, as well as those of many other wildly successful hedge fund managers, we have to wonder whether something else is going on. The Efficient Markets Hypothesis poses a related paradox for investors. In 1980, the economists Sanford Grossman and Joseph Stiglitz argued that without the opportunity to profit from market imperfections, investors have no reason to gather and analyze the information the market uses to discover prices.3 After all, what would be the point? The price discovery process isn’t free, and in the absence of economic incentives—in other words, profit opportunities, also known as market inefficiencies—liquid financial markets will simply cease to exist. According to Grossman and Stiglitz, a perfectly efficient market is, in fact, impossible. The Adaptive Markets Hypothesis elegantly resolves these difficulties by first observing that prices don’t automatically reflect all available information—how could they?

High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

Trading algorithms have very small reaction times to fluctuations in liquidity and price. Yet, the markets remain largely stable, with bounded bid–ask spread and price volatility. This is primarily due to the heterogeneous return objectives and investment horizons of the market participants. Agent heterogeneity has also created the highfrequency trading (HFT) debate about the value that low latency machine trading adds to the investment and price discovery process. Here we take the point of view that HFT is a market fact. Our objective is to understand its potential and limitations. 21 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 22 — #42 i i HIGH-FREQUENCY TRADING From the point of view of an executing broker, there are four main types of clients or market agents: 1. institutional investors, such as pension funds and asset management firms; 2. quant funds, including market makers that seek to capture the bid–ask spread or exchange rebates; 3. retail investors, driven primarily by private liquidity demand and less by proprietary signals; 4. hedgers of equity exposures, such as derivatives desks.

On average, algorithmic trading participation reduces the degree of autocorrelation in high-frequency currency returns by posting quotes that reflect new information more quickly. Finally, Chaboud et al report highly correlated algorithmic trading behaviour in response to an increase in absolute value of the autocorrelation in high-frequency currency returns; this supports the concern that high-frequency traders have very similar strategies, which may hinder the price discovery process (Jarrow and Protter 2011). HFT during time of market stress The availability of liquidity has been examined in equity markets; academic studies indicate that, on average, high-frequency traders provide liquidity and contribute to price discovery. These studies show that high-frequency traders increase the overall market quality, but they fail to zoom in on extreme events, where their impact may be very different.

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, computerized markets, computerized trading, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, prediction markets, price discovery process, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day

Overall, dark pools and broker internalization facilities aren’t unquestionably bad, but it’s hard to make a compelling case for any significant benefit. For professionals in particular, they make it easier to shoot oneself in the foot. For the public, the lack of transparency doesn’t inspire confidence. And for the markets themselves, there is a legitimate question about whether or not they detract from the price discovery process. For these reasons, I believe that the default destination for retail customer orders should always be the public markets. If customers want to “opt in” and select a dark pool or internalizer for their orders, that’s fine, but it should be a choice the customer makes – not a choice that the broker makes for them. The Impossibility of Getting Full and Precise Information Lewis, now embedded in the offices of IEX, walks us through a discussion of how IEX might provide information to the customers of the banks about how the banks and IEX handle their orders.

Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, buy and hold, compound rate of return, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

Prior to early 2001, stock prices in the United States were quoted in multiples of one-sixteenth and one-eighteenth of a penny. Since April 9, 2001, all U.S. stocks have been quoted in decimals. This seemingly innocuous change has had a dramatic impact on the market structure, which is particularly negative for the profitability of statistical arbitrage strategies. The reason for this may be worthy of a book unto itself. In a nutshell, decimalization reduces frictions in the price discovery process, while statistical arbitrageurs mostly act as market makers and derive their profits from frictions and inefficiencies in this process. (This is the explanation given by Dr. Andrew Sterge in a Columbia University financial engineering seminar titled “Where Have All the Stat Arb Profits Gone?” in January 2008. Other industry practitioners have made the same point to me in private conversations.)

pages: 1,164 words: 309,327

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

active measures, Andrei Shleifer, asset allocation, automated trading system, barriers to entry, Bernie Madoff, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, fault tolerance, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, interest rate swap, invention of the telegraph, job automation, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, Nick Leeson, open economy, passive investing, pattern recognition, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, race to the bottom, random walk, rent-seeking, risk tolerance, risk-adjusted returns, selection bias, shareholder value, short selling, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game

These markets have trading rules that specify how they arrange their trades. Their order precedence rules determine which buyers trade with which sellers, and their trade pricing rules determine the trade prices. Most order-driven markets are auction markets. In an auction market, the trading rules formalize the process by which buyers seek the lowest available prices and sellers seek the highest available prices. Economists call this the price discovery process because it reveals the prices that best match buyers to sellers. In order-driven markets, traders can offer or take liquidity. Traders who offer liquidity indicate the terms at which they will trade. Traders who take liquidity accept those terms. Dealers can—and often do—trade in order-driven markets. In pure order-driven markets, they trade on an equal basis with all other traders. In some order-driven markets, dealers provide most of the liquidity.

Traders must control their inventories to avoid inventory risk. When dealer inventories are in balance, dealers want to buy and sell in equal quantities so that their inventories remain near their target levels. A two-sided order flow includes a mix of buyers and sellers who want to trade equal quantities. Dealers try to set their prices to obtain two-sided order flows. The search for prices that produce a two-sided order flow is called the price discovery process. Dealers try to discover the prices which ensure that buying and selling quantities are just in balance. At these prices, supply equals demand. Prices that balance supply and demand determine market values. Dealers try to discover market values. Dealing is most profitable when dealers can sell immediately after buying and buy immediately after selling. Dealers profit from these round-trip transactions if they can buy at lower prices than those at which they can sell.

See trading posts post-trade transparency, 101 power of test, 454–57 prearranged trading, 165, 166 precommitted liquidity suppliers, 400, 404, 406 preferencing, 161–63, 282, 514, 515, 520–22, 528–29 preferred stocks, 40 premiums, 75 pre-trade transparency, 101 price(s) accelerator, 556 ask, 5, 69, 280, 295 benchmark, 422, 423–32, 433 bid, 5, 69, 70, 280, 295 clustering, 91 correlations, 8 dealer mistakes, 291–92 derivative, 132–37 discrimination, 247, 323, 325–26, 332 formation in index markets, 489–91 and fundamental values, 403 indexes, 484–86 inferior, 70 informative, 4, 206–14, 218, 222, 224, 235, 237–39, 241, 243 and limit orders, 76–77 manipulation, 135–37, 256 in market-based economies, 208–9 market-if-touched orders, 80 for perishable commodities, 416 predicting, 442 public benefits from informative, 206–14 reflection of information, 229 stock, 211–12 and stop orders, 78–79 terminology, 69–70 tick-sensitive orders, 81 trade, 70 and trading exposure, 385–86 unexpected increases, 370, 372 volatility, 76 price and sale feeds, 98 price characterization of arbitrage, 375 price concessions, 72, 324 price continuity, 497, 498 price convergence, 348, 350 price discovery process, 94, 284 price impact. See market impact price improvement, 71, 72, 282, 515 price limits, 572, 573–75 price manipulators, 195, 196, 259 price priority, 113, 117, 334 price reversal, 432, 434, 497 price risk, 183 price-weighted index, 485, 486 primary capital markets, 209, 210, 211 primary government securities dealers, 58 primary listing markets, 48, 49 primary markets, 39 primary order precedence rules, 117 primary spread determinants, 311, 312–13 Primex Auction System, 309, 515 principal-agent problem, 8, 159 principal trading, 149 principal value, 40 printing a trade, 333 private benefits, 205–6 private information, 223 private services, 538 proactive traders, 383, 384 production/allocation decisions, 206–7, 208 profit-motivated traders, 177, 194–97, 198, 205, 206 ProFunds Ultra OTC Fund, 447 program trading, 368, 489 proprietary orders, 70 proprietary traders, 32 proprietary trading, 32, 149 pro rata allocation, 117, 134, 447 proxies asymmetric information, 314–15 for utilitarian trading interest, 316–17 volatility, 315–16 proxy variables, 312 pseudo-informed traders, 197, 229–30, 231 public benefits definition of, 205 of exchange, 214 of hedging, 214 from informative prices, 206–14 of liquid markets, 214–16 of risk sharing, 214–15 of trading, 206 public commodity pools, 474 public goods, 494–95 public information, 223, 241–43 public liquidity preservation principle, 499, 500 public order precedence rule, 113, 115, 500 public policy, 529–30, 535 public services, 538 public traders, 310–11, 313, 498 pure arbitrageurs, 194 pure discount bonds.

pages: 321

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

algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, 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 trading, text mining, transaction costs, Vanguard fund, yield curve

MARKET MICROSTRUCTURE AND EXPECTED RETURNS Apart from allowing us to model the dynamics of liquidity, intraday data enables analysis of the interaction among market participants. In fact, the goal of the latter research direction is often set to find theoretical explanations for empirical patterns documented by the former. 212 Finding Alphas The theories of microstructure take into account the price discovery process and, in general, differentiate among informed traders, uninformed traders, and specialists. Informed traders are defined as rational entities that buy (or sell) if the “true value” of an asset is higher (or lower) than the bid (or ask) price. Uninformed traders, by contrast, act on no rational logic but trade purely for liquidity purposes. For example, they buy into equities in periods when they have excess income and sell when they are in need of cash.

pages: 236 words: 77,735

Rigged Money: Beating Wall Street at Its Own Game by Lee Munson

affirmative action, asset allocation, backtesting, barriers to entry, Bernie Madoff, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, call centre, Credit Default Swap, diversification, diversified portfolio, estate planning, fiat currency, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, High speed trading, housing crisis, index fund, joint-stock company, money market fund, moral hazard, Myron Scholes, passive investing, Ponzi scheme, price discovery process, random walk, risk tolerance, risk-adjusted returns, risk/return, stocks for the long run, stocks for the long term, too big to fail, trade route, Vanguard fund, walking around money

He gives the broker an order to buy 100,000 shares of Acme Corporation. It doesn’t matter if it is a mutual fund, registered investment adviser (RIA), or a hedge fund, the point is that when dealing with a pool of money, the trades get big. This affects your money regardless of whether you own individual stocks or pool it with a manager. The market doesn’t care who you are. Now we enter the point of price discovery. price discovery The process of determining the price of an asset in the marketplace through the interactions of buyers and sellers. Also known as reality versus what you think a security is worth. You may discover that nobody wants to buy your stock unless the price is very low. Or, you may discover that in order to buy a stock you must pay a premium to get someone to sell it to you. market maker A dying breed.

pages: 537 words: 144,318

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

Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, 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, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, 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, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

Then they hide it through beta, sometimes through difficult-to-see exotic betas. Alpha seeking is, however, a positive sum game for society. You need to have people in there chasing alpha to make markets more efficient. And by efficiency I am not talking about providing liquidity to the market. Rather, you need to have people constantly trying to evaluate the right price, who are ready to trade on that belief, pushing the market towards equilibrium in a price discovery process. That way we get better allocation of resources in the real economy and fewer bubbles. If there had been more John Paulsons in the market during the last few years, and fewer gullible institutional investors in subprime, the global economy would have been much better off. But alpha in a strict sense is a zero-sum game, although with beneficial externalities for society. What else did you learn in 2008?

pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond

While the market price (shown by the dashed line) moves up as a result of the catalyst, it initially underreacts and therefore continues to go up for a while. A trend-following strategy buys the asset as a result of the initial upward price move and therefore capitalizes on the subsequent price increases. At this point in the life cycle, trend-following investors contribute to the speeding up of the price discovery process. Figure 12.1. Stylized plot of the life cycle of a trend. Source: Hurst, Ooi, and Pedersen (2013). Research has documented a number of behavioral tendencies and market frictions that lead to this initial underreaction:3 i. Anchor-and-insufficient-adjustment. People tend to anchor their views to historical data and adjust their views insufficiently to new information. ii. The disposition effect.

pages: 468 words: 145,998

On the Brink: Inside the Race to Stop the Collapse of the Global Financial System by Henry M. Paulson

asset-backed security, bank run, banking crisis, break the buck, Bretton Woods, buy and hold, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, Doha Development Round, fear of failure, financial innovation, fixed income, housing crisis, income inequality, London Interbank Offered Rate, Long Term Capital Management, margin call, money market fund, moral hazard, Northern Rock, price discovery process, price mechanism, regulatory arbitrage, Ronald Reagan, Saturday Night Live, short selling, sovereign wealth fund, technology bubble, too big to fail, trade liberalization, young professional

Investors ran away from securities that made them nervous—driving the current yield of 30-day ABCP up to 6 percent (from 5.28 percent in mid-July)—and began to accumulate Treasury bonds and notes, long the safest securities on the planet. This classic flight-to-quality nearly resulted in a failed auction of four-week bills on August 21, when massive demand for government paper so muddied the price discovery process that, ironically, some dealers pulled back from bidding to avoid potential losses. As a result, there were barely enough bids to cover the auction, so yields shot up despite the strong real demand. Karthik Ramanathan, head of Treasury’s Office of Debt Management, had to reassure global investors that the problems stemmed from too much demand, not too little. In the end, the Treasury auctioned off $32 billion in four-week bills at a discount rate of 4.75 percent, nearly 2 percentage points higher than the prior day’s closing yield.

pages: 217 words: 61,407

pages: 310 words: 90,817

Paper Money Collapse: The Folly of Elastic Money and the Coming Monetary Breakdown by Detlev S. Schlichter

bank run, banks create money, British Empire, business cycle, capital controls, Carmen Reinhart, central bank independence, currency peg, fixed income, Fractional reserve banking, German hyperinflation, global reserve currency, inflation targeting, Kenneth Rogoff, Kickstarter, Long Term Capital Management, market clearing, Martin Wolf, means of production, money market fund, moral hazard, mortgage debt, open economy, Ponzi scheme, price discovery process, price mechanism, price stability, pushing on a string, quantitative easing, reserve currency, rising living standards, risk tolerance, savings glut, the market place, The Wealth of Nations by Adam Smith, Thorstein Veblen, transaction costs, Y2K

The quick and, indeed, instantaneous change in most prices that can be assumed in a model economy will communicate the facts quickly to everybody. The time and space for some consumers and some producers to err and to develop the patterns described above is very restricted in the context of pure models. However, the more we move away from the unrealistic model assumptions and consider a real-life economy, in which spending is not ongoing but discontinuous and intermittent, and price-discovery therefore periodic, these processes will be unavoidable. In any case, the imperfections of a real-life economy, when compared to the purity of the theoretical model, enhance the phenomena we just described; they do not cause them. What causes the processes described here is the lack of full transparency, which leads to potential misinterpretation. Some economic agents will confuse additional nominal spending with a rise in real demand that can only result from changes in consumer preferences or true entrepreneurial success.

pages: 209 words: 13,138

pages: 444 words: 128,701