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

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affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, 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, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, labour mobility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

A trader’s size affects market prices, as does the quantity of similar traders in a space. 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. We had some analytics to figure out the deterioration of Sharpe ratio due to more assets chasing the same strategy.

chincarinil@hotmail.com Cast of Characters Herbert Allison: President and COO of Merrill Lynch during the LTCM crisis. 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. Warren Buffett: Billionaire investor and CEO of Berkshire Hathaway.

John Maynard Keynes: British economist who first mentioned ideas of quantitative easing. 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. John Mack: CEO and Chairman of Morgan Stanley during the financial crisis. Deryck Maughan: Chairman and CEO of Salomon Brothers from 1992 to 1997, Vice Chairman of Citigroup from 1998 to 2004, Vice Chairman of the NYSE from 1996 to 2000, and currently partner at KKR.

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

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algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, compound rate of return, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, 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, systematic trading, transaction costs

Is there anything common between managing a $100 million portfolio and managing a $100,000 portfolio? 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. Of course, there are also plenty of counterexamples, but clearly this is a possible route to riches as well as intellectual accomplishment, and for someone with an entrepreneurial bent, a preferred route.

An example of this is the summer 2007 meltdown, described in the previously cited article “What Happened to the Quants in August 2007?” by Amir Khandani and Andrew Lo. 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. It therefore became a classic study of financial contagion as propagated by hedge funds.

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

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Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, Bretton Woods, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, Kenneth Rogoff, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, moral hazard, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, technology bubble, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs

In similar fashion, the Nobel laureates Myron Scholes and Robert Merton, whose formula for pricing options grew out of the efficient-markets school, signed up with the hedge fund Long-Term Capital Management. 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.

Then came the crisis of 2007–2009, and every judgment about finance was thrown into question. 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. Hedge funds needed access to leverage, but nobody lent to anyone in the weeks after the Lehman shock.

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. Mirrors had been positioned at critical corners so you could see if Brown was coming.


pages: 402 words: 110,972

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

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AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, butterfly effect, buttonwood tree, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, Internet Archive, John Nash: game theory, Khan Academy, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, moral hazard, mutually assured destruction, natural language processing, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, Richard Stallman, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra

They were looking to make a boatload of cash, and willing to commit firm capital to do so.4 Nunzio Tartaglia, a Jesuit-educated Ph.D. physicist with the vocabulary of a sailor, started an automated trading group at Morgan Stanley in the mid-1980s. 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. Finding stronger short-term correlations than the next guy had big rewards.

Fischer Black, after leaving MIT for Goldman Sachs, said, “Markets look a lot more efficient from the banks of the Charles than from the banks of the Hudson.”3 Someone gets to pick up that $100 bill. 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. There was a general expectation that the new country and the states would default on the substantial debt.


pages: 204 words: 58,565

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

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

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. You would be surprised how often they can’t give me a clear answer.” Summers was reportedly paid $5 million for playing this role, so it must have been perceived as valuable.


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

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Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, Black Swan, 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, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, systematic trading, The Myth of the Rational Market, time value of money, transaction costs

A PEEK INSIDE THE BLACK BOX Many investors legitimately fear that quantitative analysis is an inscrutable “black box,” from which emanate incomprehensible investment ideas, many of which don't look like winners. 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

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Affordable Care Act / Obamacare, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Emanuel Derman, housing crisis, illegal immigration, Internet of things, late fees, 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, recommendation engine, Sharpe ratio, statistical model, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor

How likely is it that a mortal threat will arise in the next year or two? 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. They had their own risk teams, and they bought our product mostly to look good for investors.


pages: 317 words: 84,400

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

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23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Mark Zuckerberg, market bubble, medical residency, Narrative Science, PageRank, pattern recognition, Paul Graham, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, 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

And some states, including New York, have ordered 23andMe and similar services to get approval from the state’s health department, declaring their tests to be medical and therefore open to regulation. 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. The information from sequencing just hundreds of people’s complete DNA genomes produces data so copious that researchers usually don’t send it to others across the Internet because such a transfer would take weeks.


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

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algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, cloud computing, collapse of Lehman Brothers, Donald Trump, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, 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, transaction costs, Watson beat the top human players on Jeopardy!

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. The pair eventually teamed up with Newton and Sturges. In 2005, using funds from Fleiss’s stock market bets in college, which were based on an algorithm he’d designed, they set up shop in a six-hundred-square-foot office on 42nd Street in midtown Manhattan.


pages: 374 words: 114,600

The Quants by Scott Patterson

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Albert Einstein, asset allocation, automated trading system, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Nash: game theory, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, NetJets, new economy, offshore financial centre, Paul Lévy, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, 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: 471 words: 124,585

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

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Admiral Zheng, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, collateralized debt obligation, colonial exploitation, Corn Laws, corporate governance, 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, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, German hyperinflation, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, interest rate swap, Isaac Newton, iterative process, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labour mobility, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Naomi Klein, Nick Leeson, Northern Rock, 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, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, structural adjustment programs, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, too big to fail, transaction costs, value at risk, Washington Consensus, Yom Kippur War

Meriwether himself, born in 1947, ruefully observed: ‘If I had lived through the Depression, I would have been in a better position to understand events.’100 To put it bluntly, the Nobel prize winners had known plenty of mathematics, but not enough history. 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. Far from declining, in the past ten years hedge funds of every type have exploded in number and in the volume of assets they manage.


pages: 380 words: 118,675

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

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3D printing, airport security, AltaVista, Amazon Mechanical Turk, Amazon Web Services, bank run, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, call centre, centre right, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, Douglas Hofstadter, Elon Musk, facts on the ground, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, Kevin Kelly, Kodak vs Instagram, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Network effects, new economy, optical character recognition, pets.com, Ponzi scheme, quantitative hedge fund, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Thomas L Friedman, Tony Hsieh, Whole Earth Catalog, why are manhole covers round?

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: 527 words: 147,690

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

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23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, 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: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

If you have something that gets them 80 percent of their way there, it’s an infinite improvement and they will be so happy. The number of industries where the difference between 85 versus 90 percent accuracy is the rate-limiting factor is very small. Sometime in the future, after everyone has adopted these sorts of technologies, the predictive accuracy will start to matter, but at this point it doesn’t matter as much as people think it does. Sure there are some areas like quantitative hedge funds that are fighting tooth and nail over that last epsilon, but most people are not in that position. So it really comes back to the question of “What value are we providing?” Gutierrez: How do you view and measure success now that you’ve transitioned back to research? Jonas: As I’ve transition back to research, it’s been very important for me to keep the startup experience view and measurement of success at the top of my mind.