quantitative hedge fund

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pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, Alan Greenspan, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bear Stearns, Bernie Madoff, Black-Scholes formula, Bob Litterman, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, Glass-Steagall Act, global macro, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low interest rates, low skilled workers, managed futures, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, National best bid and offer, negative equity, Northern Rock, Occupy movement, oil shock, price stability, proprietary trading, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, tail risk, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Thus, traders must think of market prices as endogenous rather than exogenous, especially in saturated trading spaces. By 2007, the market had more quantitative hedge funds than it used to, so the space was more crowded. Hedge funds running similar strategies in similar ways helped cause the 2007 quant crisis. Large quant funds must be thoughtful in using well-known strategies. Most less-sophisticated players also use these strategies, making funds more correlated. The former co-head of one of the world’s largest quantitative hedge funds commented on these lessons: With the benefit of hindsight, our firm and the industry was too big. There are subtle issues that arise when you are a big firm.

Appointed President and CEO of Fannie Mae in September 2008 and is currently the Assistant Secretary of the Treasury for Financial Stability of the United States. Madelyn Antoncic: Managing Director and Chief Risk Officer at Lehman Brothers during the financial crisis. Cliff Asness: Co-founder and CEO of the quantitative hedge fund AQR Capital Management. Previously was Managing Director of Quantitative Research at Goldman Sachs. Ben Bernanke: Chairman of the Federal Reserve during the crisis of 2008. Lloyd Blankfein: CEO and Chairman of Goldman Sachs. Steve Blasnik: President and CEO of Parkcentral Capital Management, a relative value hedge fund that managed Ross Perot and other investors' private wealth.

Alex Kirk: Managing Director and global head of high-yield and leveraged loans at Lehman Brothers during financial crisis. William Krasker: Principal at LTCM. Modeler at LTCM. Arjun Krishnamacher: Principal at LTCM and JWMP. Ken Kroner: Head of Blackrock's global market strategies overseeing the quantitative hedge fund group. Jim Leach: Republican member of the U.S. House of Representatives for Iowa during LTCM crisis. Dick Leahy: Principal at LTCM and JWMP. Handled mortgage trading and co-managed with Meriwether the Macro fund at JWMP. Ken Lewis: CEO and Chairman of Bank of America during the financial crisis.


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

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, book value, Brownian motion, business continuity plan, buy and hold, classic study, compound rate of return, Edward Thorp, Elliott wave, endowment effect, financial engineering, fixed income, general-purpose programming language, index fund, Jim Simons, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk free rate, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

My contention is that it is much more logical and sensible for someone to become a profitable $100,000 trader before xi P1: JYS fm JWBK321-Chan xii September 24, 2008 13:43 Printer: Yet to come PREFACE becoming a profitable $100 million trader. This can be shown to be true on many fronts. Many legendary quantitative hedge fund managers such as Dr. Edward Thorp of the former Princeton-Newport Partners (Poundstone, 2005) and Dr. Jim Simons of Renaissance Technologies Corp. (Lux, 2000) started their careers trading their own money. They did not begin as portfolio managers for investment banks and hedge funds before starting their own fund management business.

During August 2007, under the ominous cloud of a housing and mortgage default crisis, a number of well-known hedge funds experienced unprecedented losses, with Goldman Sachs’s Global Alpha fund falling 22.5 percent. Several billion dollars evaporated within all of one week. Even Renaissance Technologies Corporation, arguably the most successful quantitative hedge fund of all time, lost 8.7 percent in the first half of August, though it later recovered most of it. Not only is the magnitude of the loss astounding, but the widespread nature of it was causing great concern in the financial community. Strangest of all, few of these funds hold any mortgage-backed securities at all, ostensibly the root cause of the panic.

When you have reached a point where your capacity is higher than what the Kelly formula suggests you can prudently utilize, it P1: JYS c08 JWBK321-Chan 162 August 27, 2008 15:20 Printer: Yet to come QUANTITATIVE TRADING may be time for you to start taking on investors, who will at the very least defray the costs of your infrastructure, if not provide an incentive fee. Alternatively, you might want to take your strategy (and, more importantly, your track record) to one of the larger hedge funds and ask for a profit-sharing contract. After the recent major losses at quantitative hedge funds, many people have started to wonder if quantitative trading is viable in the long term. Though the talk of the demise of quantitative strategies appears to be premature at this point, it is still an important question from the perspective of an independent trader. Once you have automated everything and your equity is growing exponentially, can you just sit back, relax, and enjoy your wealth?


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby

Alan Greenspan, Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Bear Stearns, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, book value, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, deal flow, do well by doing good, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, Jim Simons, John Bogle, John Meriwether, junk bonds, Kenneth Rogoff, Kickstarter, Long Term Capital Management, low interest rates, machine translation, margin call, market bubble, market clearing, market fundamentalism, Market Wizards by Jack D. Schwager, Mary Meeker, merger arbitrage, Michael Milken, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, operational security, pattern recognition, Paul Samuelson, pre–internet, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Savings and loan crisis, Sharpe ratio, short selling, short squeeze, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, tail risk, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs, two and twenty, uptick rule

Andrei Shleifer, the Harvard economist who had compared the efficient-market theory to a crashing stock, helped to create an investment company called LSV with two fellow finance professors. His coauthor, Lawrence Summers, made the most of a gap between stints as president of Harvard and economic adviser to President Obama to sign on with D. E. Shaw, a quantitative hedge fund.9 Yet the biggest effect of the new inefficient-market consensus was not that academics flocked to hedge funds. It was that institutional investors acquired a license to entrust vast amounts of capital to them. Again, the years after the 1987 crash were an inflection point. Before, most money in hedge funds had come from rich individuals, who presumably had not heard academia’s message that it was impossible to beat the market.

Whereas the market disruptions of the 1990s could be viewed as a tolerable price to pay for the benefits of sophisticated and leveraged finance, the convulsion of 2007–2009 triggered the sharpest recession since the 1930s. Inevitably, hedge funds were caught up in the panic. In July 2007, a credit hedge fund called Sowood blew up, and the following month a dozen or so quantitative hedge funds tried to cut their positions all at once, triggering wild swings in the equity market and billions of dollars of losses. The following year was more brutal by far. The collapse of Lehman Brothers left some hedge funds with money trapped inside the bankrupt shell, and the turmoil that followed inflicted losses on most others.

The place felt like an upmarket science facility—comfortable, low-key, eerily clean—and on the door of one office along an antiseptic corridor, somebody had stuck an article with the title “Why Most Published Research Findings Are False.” The windowless rooms that housed racks of computer servers were guarded with elaborate key systems, but the facility’s most striking feature was its openness. Whereas other quantitative hedge funds enforced fierce internal Chinese walls, doling out information to employees on a need-to-know basis in an effort to protect secrets, the atmosphere at Renaissance was altogether different. The scientists roamed the corridors freely, constrained only by the danger that Peter Brown would crash into them on his unicycle.


pages: 304 words: 99,836

Why I Left Goldman Sachs: A Wall Street Story by Greg Smith

Alan Greenspan, always be closing, asset allocation, Bear Stearns, Black Swan, bonus culture, break the buck, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, East Village, fear index, financial engineering, fixed income, Flash crash, glass ceiling, Glass-Steagall Act, Goldman Sachs: Vampire Squid, high net worth, information asymmetry, London Interbank Offered Rate, mega-rich, money market fund, new economy, Nick Leeson, proprietary trading, quantitative hedge fund, Renaissance Technologies, short selling, short squeeze, Silicon Valley, Skype, sovereign wealth fund, Stanford marshmallow experiment, statistical model, technology bubble, too big to fail

I was immediately excited. When the partner in charge of Derivatives Sales offers you an opportunity like this, I thought, you jump on it. I jumped on it. The new job was an opportunity to learn a broader set of derivatives products and diversify my client base into sovereign wealth funds, quantitative hedge funds, and state pension plans. Also, I thought it would be fun. And it worked out. The markets turned around in 2005, and our little Derivatives Sales team—Laura, Connors, and me—began to fire on all cylinders. The fanatical attention to detail that Corey had taught me helped our desk consolidate our gains.

The markets kept booming, and Derivatives Sales continued to rack up revenues. Clients were confident: they were trading; they were taking risk. I was executing well for them, which led to more business, because they knew I was looking after them. My clients included some of the biggest asset managers, quantitative hedge funds, and sovereign wealth funds in the world. The commission business—flat, transparent fees on agency orders in things such as futures, exchange-traded funds, and options—was flourishing. The cash register was ringing. But change was in the air. At the end of May, our CEO, Hank Paulson, was appointed U.S. secretary of the treasury, and Lloyd Blankfein became CEO and chairman of Goldman Sachs.

It is not the most glamorous work, but make no mistake: quants can be worth their weight in gold; the best ones get paid millions of dollars. Sadly, that’s why actual rocket scientists and engineers leave their professions for the allure of making ten times as much money in finance. Some quants have gone out on their own and started quantitative hedge funds, relying on their smarts and the models they’ve built to generate outsize returns for themselves and their investors. (When you hear someone on Wall Street talk about a “black box,” there isn’t an actual box. It is the computer model that one of these quants has built.) It was my job to cover the biggest quant funds on the Street—in particular, Goldman’s flagship fund, Global Alpha, run by Mark Carhart and Ray Iwanowski; AQR Capital, run by Cliff Asness; and Bridgewater Associates, run by Ray Dalio.


System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

In New York during that same period, a thirty-year-old named Jeff Bezos began to realize the commercial possibilities of the internet. Bezos, who had graduated from Princeton University in 1986 with degrees in electrical engineering and computer science, had originally gone to Wall Street to use his analytical skill at quantitative hedge funds such as D. E. Shaw & Co., where he quickly rose to the position of vice president. Working through the decision as to whether he should stay at his lucrative job or make the jump to starting an internet company, he invoked a “regret minimization framework”: he based his decision on minimizing the future regret he might have with regard to the decision he was making at the time.

Only when the system isn’t able to get you the help you need will a human be brought in to intervene. That seems very likely to reduce the number of customer support representatives needed. How about the world of finance? Increasingly, AI is used to power ever-more-sophisticated models for trading equities, commodities, and derivatives. Called “quantitative hedge funds,” these systems use algorithmic techniques, frequently powered by machine learning models, to make split-second transactions on everything from Apple stock to zinc futures. One of the pioneers in this area, Renaissance Technologies, was founded by Jim Simons, a mathematics PhD who had spent time working at the National Security Agency on code breaking—a somewhat different but equally quantitative form of pattern recognition.

., 71, 166 Stanford panel discussion on, 119–20 technology and biomedical research, 129–33 See also facial recognition technology private right of action in California, 147 procedural fairness, 92–93 professional ethics, xxx–xxxi, 244 ProPublica investigation of racism in Florida’s criminal court, 97 Public Access to Court Electronic Records (PACER), xxiii public health vs. factory farms, 20–21 Putin, Vladimir, 84 quantitative hedge funds using AI, 163–64 Rabois, Keith, 191–92 racial discrimination in machine-based recruiting system, 80–81 Rahwan, Iyad, 179 random “noise” in data, 131–32 randomized response survey design, 131–32 Rawls, John, 93–94 Reagan, Ronald, 258–59 Really Simple Syndication (RSS) specification, xxii recruiting system with gender bias, 81–82, 83 Reddit, xxii, 44 regret minimization framework, 30 regulations overview, 53 antitrust-based, 55–59, 227–28 based on OTA nonpartisan information, 258 constrain market dominance of big tech companies, 256–57 for creating healthy market competition, 229–30 government’s complicity in absence of, 59–63 matching tech sector innovation with appropriate regulatory structures, 261 medical research and education regulations, 244–51 policy-makers’ dilemma, 56–57 and politicians’ limited tech knowledge, 52–53 on tech start-ups, 45 technical ignorance of lawmakers, 71–72 technology companies lobby for autonomy, 46 when social consequences become intolerable, 55 Reich, Rob, xvi, 22–23 Renaissance Technologies, 163–64 representational adequacy, 13 Republic, The (Plato), 65–66 résumés and algorithmic decision-making, 82–87, 109 revenue optimization process, 43–45 Rhinehart, Rob, 7–9, 20 road safety, 154–55, 239 role of citizens in a democracy, xiv Romer, Paul M., 58–59, 176 Rush, Benjamin, 3 safety, 53–54, 55, 133–34, 154–55, 239 Sahami, Mehran, xv, 27, 38 Samuel, Arthur, 84, 156 San Bernardino, California, terrorist attack, 72, 134–35 Sanger, Larry, 195 Schatz, Brian, 240 Scholes, Myron, 59 search engine optimization (SEO), 196 Section 230, Communications Decency Act (CDA 230), 62, 221–23, 225 security, 20, 56, 71–72, 111–14, 116–17, 121–25, 128–29, 134–35, 141, 156, 166, 258 self-driving vehicles DARPA Grand Challenge, 153–54 effect on truck drivers and trucking industry, 175 human nature vs., 155–56 human pleasure from driving, 172 safety of, 154–55 in Silicon Valley, 186 society’s response to, 175 Sen, Amartya, 74, 172–73 separations regime, 229 September 11, 2001, terrorist attacks, 116 Sherman, John, 228 Sherman, Rob, 119–20 Shklar, Judith, 76 Siebel, Michael, 40 Sifton, Sam, 9 Silicon Valley, xiv, xxv–xxvi, 28, 40–41, 73 Silverberg, Nicole, 188 Simplex algorithm, 12 smart machines and humans, 153–86 overview, 160–65 autonomous weapons, 177–78 benefits for people whose jobs are changed or lost, 182–86 choice of human flourishing or greater productivity, 169 deep learning concept, 161–62 ethics and AI, 165–66 jobs lost and other costs of adjustment, 174–76 keeping humans in the loop, 178–82 livelihood destroying potential, 170–71 overcoming poverty to promote human flourishing, 170–71 value of freedom, 172–73 virtual reality, the experience machine, 167–69 Snowden, Edward, 116, 126, 139, 143–45 social and political ethics, xxxi–xxxiii Social Dilemma, The (documentary), 238–39 “Social Responsibility of Business Is to Increase Its Profits, The” (Friedman), 37–38 social safety nets, Europe and US compared, 185–86 social science, xv–xvii, xxvi, 137–40 Software Engineering Code of Ethics, 248 Solove, Daniel, 140 Soylent, 6–9, 20 Soylent Green (film), 7–8 Spliddit’s “provably fair solutions,” 88–89 Stanford University, Stanford, California overview, xiv, xv, xvi hate speech or hateful protected speech, 191–92 panel discussion on privacy concerns, 119–20 study of libertarian attitudes of tech leaders, 52 teaching computer science with social science and ethics, 251 VCs showcasing their new companies, 42–45 start-up accelerators, 44.


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

He first introduced me to several models in theoretical biology that were particularly relevant to economics, and in 1999 he and I published a paper outlining the possibility of applying evolutionary arguments to the Efficient Markets Hypothesis.22 He has since published many more papers applying ideas in physics and biology to finance. In addition to his academic pursuits, Doyne cofounded a successful quantitative hedge fund called Prediction Company with his fellow physicist, Norman Packard. This fund uses multiple algorithmic trading strategies to make money in stock markets around the world. Based on his experience, Doyne published a fascinating article in 2002 titled “Market Force, Ecology, and Evolution,” where he developed a precise analogy between financial markets and biological ecologies.

Finally, Thursday evening, central banks around the world acted in unison and injected billions of dollars into the global banking system because of a temporary breakdown in the overnight interbank lending market. This intervention may well have relieved the pressure to liquidate various holdings among the largest banks, including statarb portfolios. Amir Khandani and I called this narrative the “Unwind Hypothesis.” This explanation meant good news and bad news for the quantitative hedge fund industry. The good news is that quantitative funds were singled out during the week of August 6, 2007, not because of a breakdown in any specific quantitative strategy, but more likely due to the sudden liquidation of a large quantitative equity market-neutral portfolio. The bad news is that these losses imply a growing systemic risk in the hedge fund sector (not to be confused with systematic risk).

But liquidity isn’t easy to measure—you can’t look it up on a Bloomberg terminal—and we may not notice its changes over time and therefore can’t adapt appropriately. This became painfully clear to Amir Khandani and me when we ran one further simulation in our August 2007 research.38 In the last chapter, I described our simulation of a daily mean-reversion strategy based on how well stocks performed the previous day. But the most sophisticated quantitative hedge funds trade from minute to minute, second to second, and nowadays, even microsecond to microsecond. So Amir and I decided to simulate a higher-frequency strategy using transaction prices for all trades in the S&P 1500, time-stamped to the nearest tenth of a second, from July 2, 2007, to September 30, 2007, a three-month period surrounding the Quant Meltdown.


pages: 296 words: 78,112

Devil's Bargain: Steve Bannon, Donald Trump, and the Storming of the Presidency by Joshua Green

4chan, Affordable Care Act / Obamacare, Ayatollah Khomeini, Bernie Sanders, Biosphere 2, Black Lives Matter, business climate, Cambridge Analytica, Carl Icahn, centre right, Charles Lindbergh, coherent worldview, collateralized debt obligation, conceptual framework, corporate raider, crony capitalism, currency manipulation / currency intervention, data science, Donald Trump, Dr. Strangelove, fake news, Fractional reserve banking, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Gordon Gekko, guest worker program, hype cycle, illegal immigration, immigration reform, Jim Simons, junk bonds, liberation theology, low skilled workers, machine translation, Michael Milken, Nate Silver, Nelson Mandela, nuclear winter, obamacare, open immigration, Peace of Westphalia, Peter Thiel, quantitative hedge fund, Renaissance Technologies, Robert Mercer, Ronald Reagan, Silicon Valley, social intelligence, speech recognition, Steve Bannon, urban planning, vertical integration

He was dressed up as one of his favorite movie characters of all-time, Brigadier General Frank Savage, the tough-as-nails commander, played by Gregory Peck, who takes over a demoralized World War II bombing unit and whips them into fighting shape in the 1949 classic Twelve O’Clock High. Ordinarily, Bannon wasn’t big into cosplay. But this was a special occasion: the annual Christmas party thrown by the reclusive billionaire Robert Mercer, an eccentric computer scientist who was co-CEO of the fabled quantitative hedge fund Renaissance Technologies. As introverted and private as Bannon was voluble and outspoken, Mercer was nonetheless a man of ardent passions. He collected machine guns and owned the gas-operated AR-18 assault rifle that Arnold Schwarzenegger wielded in The Terminator. He had built a $2.7 million model train set equipped with a miniature video camera to allow operators to experience the view from inside the cockpit of his toy engine.

“Statistical machine translation,” as the process became known, soon outpaced the old method and went on to become the basis of modern speech-recognition software and tools such as Google Translate. At Renaissance, Mercer and Brown applied this approach broadly to the markets, feeding all kinds of abstruse data into their computers in a never-ending hunt for hidden correlations. Sometimes they found them in strange places. Even by the paranoid standards of black-box quantitative hedge funds, Renaissance is notoriously secretive about its methods. But in 2010, one intriguing example of the patterns it turns up became public. As the author Sebastian Mallaby details in his history of the hedge-fund industry, More Money Than God, a group of scientists at the firm’s flagship Medallion Fund discovered a correlation between weather patterns and market performance.


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

Most senior investors were on vacation, after all, so reading into the losses didn’t seem worthwhile. By that summer, a group of quantitative hedge funds had emerged dominant. Inspired by Simons’s success, most had their own market-neutral strategies just as reliant on computer models and automated trades. In Morgan Stanley’s midtown Manhattan headquarters, Peter Muller—a blue-eyed quant who played piano at a local club in his free time—led a team managing $6 billion for a division of the bank called PDT. In Greenwich, Connecticut, Clifford Asness, a University of Chicago PhD, helped lead a $39 billion quantitative hedge-fund firm called AQR Capital Management. And in Chicago, Ken Griffin—who, in the late 1980s, had installed a satellite dish on his dormitory roof at Harvard to get up-to-the-second quotes—was using high-powered computers to make statistical-arbitrage trades and other moves at his $13 billion firm, Citadel.


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

He hired young Columbia computer science professor David Shaw. At first, a few papers about hooking Unix systems to market systems emerged. Then the former academics realized there was no alpha in publications. Shaw went on to found D.E. Shaw & Company, one of the largest and most consistently successful quantitative hedge funds. Fischer Black’s Quantitative Strategies Group at Goldman Sachs were algo pioneers. They were perhaps the first to use computers for actual trading, as well as for identifying trades. The early alpha seekers were the first combatants in the algo wars. Pairs trading, popular at the time, relied on statistical models.

Back on the banks of the Charles in Boston 25 years later, Andy Lo wrote,“Profits may be viewed as the economic rents which accrue to [the] competitive advantage of . . . superior information, superior technology, financial innovation. . . .”4 If this conjures up images of ever faster, better, larger computing engines at giant quantitative hedge funds, you are getting the message. But this idea is not suddenly true today; it has been true forever. Innovations used to use less electricity, though. In 1790, the technology that produced vast alpha for innovative traders was boats. After the American Revolution, war bonds were trading for less than a nickel on the dollar.


pages: 193 words: 51,445

On the Future: Prospects for Humanity by Martin J. Rees

23andMe, 3D printing, air freight, Alfred Russel Wallace, AlphaGo, Anthropocene, Asilomar, autonomous vehicles, Benoit Mandelbrot, biodiversity loss, blockchain, Boston Dynamics, carbon tax, circular economy, CRISPR, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, DeepMind, Demis Hassabis, demographic transition, Dennis Tito, distributed ledger, double helix, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Geoffrey Hinton, global village, Great Leap Forward, Higgs boson, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Webb Space Telescope, Jeff Bezos, job automation, Johannes Kepler, John Conway, Large Hadron Collider, life extension, mandelbrot fractal, mass immigration, megacity, Neil Armstrong, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, pattern recognition, precautionary principle, quantitative hedge fund, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Search for Extraterrestrial Intelligence, sharing economy, Silicon Valley, smart grid, speech recognition, Stanford marshmallow experiment, Stanislav Petrov, stem cell, Stephen Hawking, Steven Pinker, Stuxnet, supervolcano, technological singularity, the scientific method, Tunguska event, uranium enrichment, Walter Mischel, William MacAskill, Yogi Berra

These are murkier, and there is no consensus among experts on the speed of advance in machine intelligence—and indeed on what the limits to AI might be. It seems plausible that an AI linked to the internet could ‘clean up’ on the stock market by analysing far more data far faster than any human. To some extent this is what quantitative hedge funds are already doing. But for interactions with humans, or even with the complex and fast-changing environment encountered by a driverless car on an ordinary road, processing power is not enough; computers would need sensors that enable them to see and hear as well as humans do, and the software to process and interpret what the sensors relay.


pages: 519 words: 155,332

Tailspin: The People and Forces Behind America's Fifty-Year Fall--And Those Fighting to Reverse It by Steven Brill

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, airport security, American Society of Civil Engineers: Report Card, asset allocation, behavioural economics, Bernie Madoff, Bernie Sanders, Blythe Masters, Bretton Woods, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Carl Icahn, carried interest, clean water, collapse of Lehman Brothers, collective bargaining, computerized trading, corporate governance, corporate raider, corporate social responsibility, Credit Default Swap, currency manipulation / currency intervention, deal flow, Donald Trump, electricity market, ending welfare as we know it, failed state, fake news, financial deregulation, financial engineering, financial innovation, future of work, ghettoisation, Glass-Steagall Act, Gordon Gekko, hiring and firing, Home mortgage interest deduction, immigration reform, income inequality, invention of radio, job automation, junk bonds, knowledge economy, knowledge worker, labor-force participation, laissez-faire capitalism, low interest rates, Mahatma Gandhi, Mark Zuckerberg, Michael Milken, military-industrial complex, mortgage tax deduction, Neil Armstrong, new economy, Nixon triggered the end of the Bretton Woods system, obamacare, old-boy network, opioid epidemic / opioid crisis, paper trading, Paris climate accords, performance metric, post-work, Potemkin village, Powell Memorandum, proprietary trading, quantitative hedge fund, Ralph Nader, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Rutger Bregman, Salesforce, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, stock buybacks, Tax Reform Act of 1986, tech worker, telemarketer, too big to fail, trade liberalization, union organizing, Unsafe at Any Speed, War on Poverty, women in the workforce, working poor

They, too, were in it for the short term. The change was revolutionary. In 1960, stock was held by its owner for an average of eight years and four months. In 1980, the average was two years, nine months. In 2016 the average was four months, and high-frequency, split-second trading of stocks, often done by quantitative hedge funds—another creation of the knowledge economy, which buy and sell stocks based on algorithms—was estimated to account for roughly half of all stock trades. If shareholders were now in it for the short term, so, too, were the managers of the shareholders’ companies. Their compensation increasingly depended, Jensen-style, on the quarterly or annual increase in the stock price.

In 1960, stock was held: PolitiFact used historical data from the New York Stock Exchange to compute these averages: Warren Fiske, “Mark Warner Says Average Holding Time for Stocks Has Fallen to Four Months,” PolitiFact, July 6, 2016, http://www.politifact.com/​virginia/​statements/​2016/​jul/​06/​mark-warner/​mark-warner-says-average-holding-time-stocks-has-f/. In 1980, the average: Ibid. In 2016 the average: Ibid. quantitative hedge funds: Gregory Zuckerman and Bradley Hope, “The Quants Run Wall Street Now,” Wall Street Journal, May 21, 2017, https://www.wsj.com/​articles/​the-quants-run-wall-street-now-1495389108?utm_source=newsletter&utm_medium=email&utm_campaign=newsletter_axiosfutureofwork&stream=future-of-work. estimated to account for roughly half: Ryan Vlastelica, “High-Frequency Trading Has Reshaped Wall Street in Its Image,” Marketwatch, March 17, 2017, http://www.marketwatch.com/​story/​high-frequency-trading-has-reshaped-wall-street-in-its-image-2017-03-15?


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

behavioural economics, Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, data science, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Gregor Mendel, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, TED Talk, text mining, the scientific method, Thomas Davenport

As Charles Duhigg has noted in a recent book, The Power of Habit, human behavior, once established, can be remarkably persistent over time.4 That allows us to magically predict the future on the basis of the past. Some organizations utilize high-priced talent just to ask penetrating questions about assumptions. Take Larry Summers, for example. The former economic adviser to the Clinton and Obama adminstrations and former president of Harvard University has worked as an adviser to D.E. Shaw, a quantitative hedge fund. Tom ran into Summers at a social occasion and asked him what he did for the company. He said, “I go in once a week and walk around the desks of the quants who build mathematical trading models. I ask them what the assumptions are behind their models, and under what circumstances they would be violated.


pages: 233 words: 67,596

Competing on Analytics: The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris

always be closing, Apollo 13, big data - Walmart - Pop Tarts, business intelligence, business logic, business process, call centre, commoditize, data acquisition, digital map, en.wikipedia.org, fulfillment center, global supply chain, Great Leap Forward, high net worth, if you build it, they will come, intangible asset, inventory management, iterative process, Jeff Bezos, job satisfaction, knapsack problem, late fees, linear programming, Moneyball by Michael Lewis explains big data, Netflix Prize, new economy, performance metric, personalized medicine, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, RFID, search inside the book, shareholder value, six sigma, statistical model, supply-chain management, text mining, The future is already here, the long tail, the scientific method, traveling salesman, yield management

One team that has adopted the “moneyball” approach is the Boston Red Sox—a team with both analytical capabilities and the money to invest in expensive players. The Red Sox also had a business need, having failed to win the World Series for eighty-six years by the 2004 season. The Sox also exemplify another reason why organizations adopt analytical competition: new leadership. The team’s two new principal owners in 2002 were John Henry, a quantitative hedge fund manager, and Tom Werner, a television producer who had previously owned the San Diego Padres. The appeal of analytics to Henry was obvious, but Werner had also realized with the Padres that the traditional baseball establishment didn’t know as much about what led to championships as it purported to.


pages: 263 words: 75,455

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, book value, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, financial engineering, forensic accounting, Henry Singleton, hindsight bias, intangible asset, Jim Simons, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk free rate, risk-adjusted returns, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, stock buybacks, survivorship bias, systematic trading, Teledyne, The Myth of the Rational Market, time value of money, transaction costs

While we know the past results for Quantitative Value have been outstanding, it often feels like one or more of the current crop of stocks selected by the model are particularly weak and should be avoided. Surely we can pick and choose from the model's output? Jim Simons disagrees. The billionaire mathematician turned quantitative hedge fund titan, believes that much of his success is attributable to his strict adherence to the output of his models6: Did you like what the model said or did you not like what the model said? That is a hard thing to backtest. If you are going to trade using models, you just slavishly use the models; you do whatever the hell it says no matter how smart or dumb you think it might be at that moment.”


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

To come up with these odds, scientists run thousands upon thousands of simulations. There was plenty to complain about with this method, but it was a simple way to get some handle on your risk. My job was to act as a liaison between our risk management business and the largest and most discerning connoisseurs of risk, the quantitative hedge funds. I’d call the hedge funds, or they’d call me, and we’d discuss any questions they had about our numbers. As often as not, though, they’d notify me only when we’d made a mistake. The fact was, the hedge funds always considered themselves the smartest of the smart, and since understanding risk was fundamental to their existence, they would never rely entirely on outsiders like us.


pages: 258 words: 71,880

Street Fighters: The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street by Kate Kelly

Alan Greenspan, bank run, Bear Stearns, book value, buy and hold, collateralized debt obligation, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, eat what you kill, fixed income, housing crisis, index arbitrage, Long Term Capital Management, margin call, moral hazard, proprietary trading, quantitative hedge fund, Renaissance Technologies, risk-adjusted returns, shareholder value, technology bubble, too big to fail, traveling salesman

But Marano could not get things under control. The combination of falling asset prices and a huge debt load made it impossible to pay creditors back. Late in July, he reluctantly filed for bankruptcy protection for both funds. In August, market conditions turned hellacious. During the first week alone, a number of quantitative hedge funds, which rely on sophisticated mathematical predictions to make trades, sustained double-digit percentage losses. Securities firm stocks were getting hit, too. Bear had lost more than $50 per share since its spring high, and competitors like Lehman and Merrill were also getting crushed. On Friday, August 3, Standard & Poor’s confirmed what many investors and rival firms had come to believe.


pages: 337 words: 86,320

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

affirmative action, AltaVista, Amazon Mechanical Turk, Asian financial crisis, Bernie Sanders, big data - Walmart - Pop Tarts, Black Lives Matter, Cass Sunstein, computer vision, content marketing, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, desegregation, Donald Trump, Edward Glaeser, Filter Bubble, game design, happiness index / gross national happiness, income inequality, Jeff Bezos, Jeff Seder, John Snow's cholera map, longitudinal study, Mark Zuckerberg, Nate Silver, Nick Bostrom, peer-to-peer lending, Peter Thiel, price discrimination, quantitative hedge fund, Ronald Reagan, Rosa Parks, sentiment analysis, Silicon Valley, statistical model, Steve Jobs, Steven Levy, Steven Pinker, TaskRabbit, The Signal and the Noise by Nate Silver, working poor

I was quite taken during our conversation by how much respect he had for them and how many of my suggestions he was convinced they’d beaten us to. I proudly shared with him an algorithm I had devised that allowed me to obtain more complete Google Trends data. He said it was clever. When I asked him if Renaissance, a quantitative hedge fund, would have figured out that algorithm, he chuckled and said, “Yeah, of course they would have figured that out.” The difficulty of keeping up with the hedge funds wasn’t the only fundamental problem that Summers and I ran up against in using new, big datasets to beat the markets. THE CURSE OF DIMENSIONALITY Suppose your strategy for predicting the stock market is to find a lucky coin—but one that will be found through careful testing.


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

Such regulation is “appallingly paternalistic,” says 23andMe, adding that people have a right to information contained within their own genes. Such genomic scanning is now fast and affordable, thanks in part to Nick Patterson, a Wall Street hacker who after eight years at Renaissance Technologies, the quantitative hedge fund, joined up with the Broad Institute, a joint research center of Harvard and MIT, in 2001. Working at Renaissance, which makes money off of sorting data and spotting patterns that nobody else can, made Patterson the perfect person to help the Broad Institute, which was drowning in DNA data so deep that the researchers there found it to be unnavigable.


pages: 346 words: 89,180

Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake

23andMe, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, Albert Einstein, Alvin Toffler, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, Big Tech, book value, Brexit referendum, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, cloud computing, cognitive bias, computer age, congestion pricing, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial engineering, financial innovation, full employment, fundamental attribution error, future of work, gentrification, gigafactory, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, Jeremy Corbyn, job automation, Kanban, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low interest rates, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, Ocado, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, private spaceflight, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Robert Solow, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, sunk-cost fallacy, survivorship bias, tacit knowledge, tech billionaire, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, TSMC, Tyler Cowen, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game

It’s easy to imagine how low-skilled workers in developed countries might lose out if they don’t have the skills to work with computers, or if their jobs are threatened by lower-paid workers in other countries. But the way these changes would benefit only the very rich is less clear. Some of the very rich have gotten richer because of technology or because they employ cheap foreign labor. But certainly not all. For every Silicon Valley mogul or quantitative hedge fund owner, there are a lot of senior managers of what we’d think of as normal businesses among the new elite. Piketty, for example, estimates that between 60 and 70 percent of the top 0.1 percent are chief executives and other senior corporate managers. A third confounding fact is the role of housing in wealth inequality.


pages: 360 words: 101,038

The Revenge of Analog: Real Things and Why They Matter by David Sax

Airbnb, barriers to entry, big-box store, call centre, cloud computing, creative destruction, death of newspapers, declining real wages, delayed gratification, dematerialisation, deskilling, Detroit bankruptcy, digital capitalism, digital divide, Elon Musk, Erik Brynjolfsson, game design, gentrification, hype cycle, hypertext link, informal economy, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kickstarter, knowledge economy, low cost airline, low skilled workers, mandatory minimum, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, military-industrial complex, Minecraft, new economy, Nicholas Carr, off-the-grid, One Laptop per Child (OLPC), PalmPilot, Paradox of Choice, Peter Thiel, Ponzi scheme, quantitative hedge fund, race to the bottom, Rosa Parks, Salesforce, Second Machine Age, self-driving car, Sheryl Sandberg, short selling, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, technoutopianism, TED Talk, the long tail, Travis Kalanick, Tyler Cowen, upwardly mobile, warehouse robotics, Whole Earth Catalog, work culture

The speed and volume of transactions that digital technology enabled amplified the volatility of the market and the asset bubble more than anyone could have predicted. “The limitations of organic human memory and calculation used to put a cap on the intricacies of self-delusion,” wrote computer scientist and philosopher Jaron Lanier in his book You Are Not a Gadget. The rise of quantitative hedge funds turned capitalism into “search engines,” which Lanier described as the perfect “blending of cyber-cloud faith and Milton Friedman economics.” The recession hit when the real world couldn’t support the numbers being assigned to it by computers and speculators. The computer’s calculations were off, it turned out.


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

The products could be extremely risky because the blockchain mechanism made the payout nearly automatic—typically within forty-eight hours—rather than the months or years it can take a normal insurer to pay on a claim. That meant the hedge fund offering to provide the insurance needed to be ready to fulfill a claim at the drop of a hat. It was also tied to massive, in some ways unquantifiable risk. But some were intrigued. The deals had interesting, complex aspects that draw highly quantitative hedge funds like bees to honey. By the end of 2021, Schmalbach had inked six deals worth about $3 billion (that is, the products covered $3 billion worth of risk). By 2022, Schmalbach, acting again on Thomson’s advice, opened an office in Hartford, Connecticut, handily close to the state insurance regulators and a forty-five-minute drive from Greenwich.


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

At Amherst, Fleiss learned about the overwhelming success of Renaissance Technologies, the Long Island hedge fund that had started using Island in the late 1990s. While Fleiss was good at math, his skills didn’t come close to the abilities of a Jim Simons or a Peter Brown. But he did know one person he thought could go toe-to-toe with them: Spencer Greenberg. Fleiss began an aggressive campaign to convince Greenberg to help him start a quantitative hedge fund. At first, Greenberg was skeptical that higher-order math could be used on the market. But as he learned more about Renaissance, he began to think there might be more to what Fleiss was saying. While at Columbia, Greenberg started to consider various mathematical strategies that he could deploy in the market.


pages: 374 words: 114,600

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

His elite team of traders, hidden away in a small enclave on Long Island, marshaled the most mind-bending advances in science and mathematics, from quantum physics to artificial intelligence to voice recognition technology, to wring billions in profits from the market. Simons was the rare investor who could make Muller feel jaw-clenchingly jealous. The two had known each other since the early 1990s, when Muller briefly considered joining Renaissance before starting his own quantitative hedge fund inside Morgan Stanley, the giant New York investment bank. Muller’s elite trading group, which he called Process Driven Trading, was so secretive that even most employees at Morgan weren’t aware of its existence. Yet over the previous decade the group, composed of only about fifty people, had racked up a track record that could go toe-to-toe with the best investment outfits on Wall Street, cranking out $6 billion in gains for Morgan.


pages: 380 words: 118,675

The Everything Store: Jeff Bezos and the Age of Amazon by Brad Stone

airport security, Amazon Mechanical Turk, Amazon Web Services, AOL-Time Warner, Apollo 11, bank run, Bear Stearns, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, buy and hold, call centre, centre right, Chuck Templeton: OpenTable:, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, deal flow, Douglas Hofstadter, drop ship, Elon Musk, facts on the ground, fulfillment center, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, John Markoff, junk bonds, Kevin Kelly, Kiva Systems, Kodak vs Instagram, Larry Ellison, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Neal Stephenson, Network effects, new economy, off-the-grid, optical character recognition, PalmPilot, pets.com, Ponzi scheme, proprietary trading, quantitative hedge fund, reality distortion field, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, SoftBank, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, the long tail, Thomas L Friedman, Tony Hsieh, two-pizza team, Virgin Galactic, Whole Earth Catalog, why are manhole covers round?, zero-sum game

It’s the tale of how one gifted child grew into an extraordinarily driven and versatile CEO and how he, his family, and his colleagues bet heavily on a revolutionary network called the Internet, and on the grandiose vision of a single store that sells everything. PART I Faith CHAPTER 1 The House of Quants Before it was the self-proclaimed largest bookstore on Earth or the Web’s dominant superstore, Amazon.com was an idea floating through the New York City offices of one of the most unusual firms on Wall Street: D. E. Shaw & Co. A quantitative hedge fund, DESCO, as its employees affectionately called it, was started in 1988 by David E. Shaw, a former Columbia University computer science professor. Along with the founders of other groundbreaking quant houses of that era, like Renaissance Technologies and Tudor Investment Corporation, Shaw pioneered the use of computers and sophisticated mathematical formulas to exploit anomalous patterns in global financial markets.


pages: 471 words: 124,585

The Ascent of Money: A Financial History of the World by Niall Ferguson

Admiral Zheng, Alan Greenspan, An Inconvenient Truth, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, classic study, collateralized debt obligation, colonial exploitation, commoditize, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, equity risk premium, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, Future Shock, German hyperinflation, Greenspan put, Herman Kahn, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, Nelson Mandela, Nick Bostrom, Nick Leeson, Northern Rock, Parag Khanna, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, risk free rate, Robert Shiller, rolling blackouts, Ronald Reagan, Savings and loan crisis, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, stocks for the long run, structural adjustment programs, subprime mortgage crisis, tail risk, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, transaction costs, two and twenty, undersea cable, value at risk, W. E. B. Du Bois, Washington Consensus, Yom Kippur War

They had understood the beautiful theory of Planet Finance, but overlooked the messy past of Planet Earth. And that, put very simply, was why Long-Term Capital Management ended up being Short-Term Capital Mismanagement. It might be assumed that after the catastrophic failure of LTCM, quantitative hedge funds would have vanished from the financial scene. After all, the failure, though spectacular in scale, was far from anomalous. Of 1,308 hedge funds that were formed between 1989 and 1996, more than a third (36.7 per cent) had ceased to exist by the end of the period. In that period the average life span of a hedge fund was just forty months.101 Yet the very reverse has happened.


pages: 524 words: 130,909

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power by Max Chafkin

3D printing, affirmative action, Airbnb, anti-communist, bank run, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Monday: stock market crash in 1987, Blitzscaling, Boeing 747, borderless world, Cambridge Analytica, charter city, cloud computing, cognitive dissonance, Cornelius Vanderbilt, coronavirus, COVID-19, Credit Default Swap, cryptocurrency, David Brooks, David Graeber, DeepMind, digital capitalism, disinformation, don't be evil, Donald Trump, driverless car, Electric Kool-Aid Acid Test, Elon Musk, Ethereum, Extropian, facts on the ground, Fairchild Semiconductor, fake news, Ferguson, Missouri, Frank Gehry, Gavin Belson, global macro, Gordon Gekko, Greyball, growth hacking, guest worker program, Hacker News, Haight Ashbury, helicopter parent, hockey-stick growth, illegal immigration, immigration reform, Internet Archive, Jeff Bezos, John Markoff, Kevin Roose, Kickstarter, Larry Ellison, life extension, lockdown, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, Max Levchin, Menlo Park, military-industrial complex, moral panic, move fast and break things, Neal Stephenson, Nelson Mandela, Network effects, off grid, offshore financial centre, oil shale / tar sands, open borders, operational security, PalmPilot, Paris climate accords, Patri Friedman, paypal mafia, Peter Gregory, Peter Thiel, pets.com, plutocrats, Ponzi scheme, prosperity theology / prosperity gospel / gospel of success, public intellectual, QAnon, quantitative hedge fund, quantitative trading / quantitative finance, randomized controlled trial, regulatory arbitrage, Renaissance Technologies, reserve currency, ride hailing / ride sharing, risk tolerance, Robinhood: mobile stock trading app, Ronald Reagan, Sam Altman, Sand Hill Road, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, skunkworks, social distancing, software is eating the world, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TechCrunch disrupt, techlash, technology bubble, technoutopianism, Ted Kaczynski, TED Talk, the new new thing, the scientific method, Tim Cook: Apple, transaction costs, Travis Kalanick, Tyler Cowen, Uber and Lyft, uber lyft, Upton Sinclair, Vitalik Buterin, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, WikiLeaks, William Shockley: the traitorous eight, Y Combinator, Y2K, yellow journalism, Zenefits

Despite the haughty-sounding name, the company had little to do with Cambridge University—in fact, its office was in London—but Nix’s plan had been to market ideas that had been developed at Cambridge about using people’s Facebook data to guess their personality type. He would raise money from Steve Bannon and the ultra-conservative Mercer family, led by the patriarch Bob, who’d gotten rich at a quantitative hedge fund, and his daughter Rebekah. The Mercers were backers of Bannon’s news outlet, Breitbart, and had invested in Nix’s pitch to adapt the Facebook concept for use in electoral politics, enabling political campaigns to scan someone’s social network profile, predict who they were going to vote for, and serve them ads to get them to show up to the polls.


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

(Investors, some shaken by the recession, were reportedly leery of putting their money in such novel investment funds.) Most hedge funds looking at social-media data seem to be taking this kind of approach, buying packages of analysis from third-party firms. As the proverb about the California gold rush goes, it often pays more to sell the shovels than to use them to dig. But at least twelve quantitative hedge funds pay a firm called Gnip to pipe all of the over 500 million or so tweets produced each day directly into their platforms. Sentiment analysis is a perfect product for a tech industry awash in data and searching for ways to make money off it. It’s but another way in which the behaviors, actions, identities, and feelings of Internet users are being bought and sold, often without their knowledge, and put toward uncertain ends.


pages: 569 words: 156,139

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire by Brad Stone

activist fund / activist shareholder / activist investor, air freight, Airbnb, Amazon Picking Challenge, Amazon Robotics, Amazon Web Services, autonomous vehicles, Bernie Sanders, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, business climate, call centre, carbon footprint, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, company town, computer vision, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, data science, deep learning, disinformation, disintermediation, Donald Trump, Downton Abbey, Elon Musk, fake news, fulfillment center, future of work, gentrification, George Floyd, gigafactory, global pandemic, Greta Thunberg, income inequality, independent contractor, invisible hand, Jeff Bezos, John Markoff, Kiva Systems, Larry Ellison, lockdown, Mahatma Gandhi, Mark Zuckerberg, Masayoshi Son, mass immigration, minimum viable product, move fast and break things, Neal Stephenson, NSO Group, Paris climate accords, Peter Thiel, Ponzi scheme, Potemkin village, private spaceflight, quantitative hedge fund, remote working, rent stabilization, RFID, Robert Bork, Ronald Reagan, search inside the book, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, SoftBank, SpaceX Starlink, speech recognition, Steve Ballmer, Steve Jobs, Steven Levy, tech billionaire, tech bro, techlash, TED Talk, Tim Cook: Apple, Tony Hsieh, too big to fail, Tragedy of the Commons, two-pizza team, Uber for X, union organizing, warehouse robotics, WeWork

Lisa Utzschneider, Amazon’s longtime global VP of ad sales, who had previously reported directly to S-team member Jeff Blackburn, quit in frustration six months later to work for Yahoo. Amazon’s nascent advertising effort was in tatters. Kotas had joined Amazon in 1999 from D. E. Shaw, the Wall Street quantitative hedge fund where Bezos had conceived the original idea for an online bookseller. He liked to tell employees the story of how Bezos had actually recruited him to join Amazon in 1997. His bags were packed for Seattle when he decided to stay at the hedge fund at the last minute. The change of heart ended up costing Kotas millions.


pages: 741 words: 199,502

Human Diversity: The Biology of Gender, Race, and Class by Charles Murray

23andMe, affirmative action, Albert Einstein, Alfred Russel Wallace, Asperger Syndrome, assortative mating, autism spectrum disorder, basic income, behavioural economics, bioinformatics, Cass Sunstein, correlation coefficient, CRISPR, Daniel Kahneman / Amos Tversky, dark triade / dark tetrad, domesticated silver fox, double helix, Drosophila, emotional labour, epigenetics, equal pay for equal work, European colonialism, feminist movement, glass ceiling, Gregor Mendel, Gunnar Myrdal, income inequality, Kenneth Arrow, labor-force participation, longitudinal study, meritocracy, meta-analysis, nudge theory, out of africa, p-value, phenotype, public intellectual, publication bias, quantitative hedge fund, randomized controlled trial, Recombinant DNA, replication crisis, Richard Thaler, risk tolerance, school vouchers, Scientific racism, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Skinner box, social intelligence, Social Justice Warrior, statistical model, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, the scientific method, The Wealth of Nations by Adam Smith, theory of mind, Thomas Kuhn: the structure of scientific revolutions, twin studies, universal basic income, working-age population

In a population of 250 million adults—roughly the number of Americans ages 20 and older—the top 0.1 percent amounts to 250,000 people. Think about the 250,000 people with the nation’s highest visuospatial and math skills. They constitute some substantial proportion of the top programmers and hardware designers in Silicon Valley, the staffs of quantitative hedge funds, and the nation’s most eminent mathematicians, physicists, chemists, biologists, and engineers. Apply the same logic to other fields requiring different abilities—the 250,000 most gifted attorneys, the 250,000 most gifted managers, the 250,000 most gifted in the performing arts. America is far from having tapped the talents of all 250,000 of the most gifted in any field, but it is probable that a large proportion of the most important accomplishments in all fields are done by those who are in the top 0.1 percent, three standard deviations above the mean.