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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, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative ﬁnance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

The computer algorithms are designed and perhaps programmed by the traders themselves, based on the historical performance of the encoded strategy tested against historical financial data. Is quantitative trading just a fancy name for technical analysis, then? Granted, a strategy based on technical analysis can be part of a quantitative trading system if it can be fully encoded as computer programs. However, not all technical analysis can be regarded as quantitative trading. For example, certain chartist techniques such as “look for the formation of a head and shoulders pattern” might not be included in a quantitative trader’s arsenal because they are quite subjective and may not be quantifiable. Yet quantitative trading includes more than just technical analysis. Many quantitative trading systems incorporate fundamental data in their inputs: numbers such as revenue, cash flow, debt-toequity ratio, and others.

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This is also a dangerous emotion to bring to independent quantitative trading. As I hope to persuade you in this chapter and in the rest of the book, instant wealth is not the objective of quantitative trading. The ideal independent quantitative trader is therefore someone who has some prior experience with finance or computer programming, who has enough savings to withstand the inevitable losses and periods without income, and whose emotion has found the right balance between fear and greed. THE BUSINESS CASE FOR QUANTITATIVE TRADING A lot of us are in the business of quantitative trading because it is exciting, intellectually stimulating, financially rewarding, or perhaps it is the only thing we are good at doing. But for others who may have alternative skills and opportunities, it is worth pondering whether quantitative trading is the best business for you.

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Increasingly, however, I have found that many strategies described by academics are either too complicated, out of date (perhaps the once-profitable strategies have already lost their power due to competition), or require expensive data to backtest (such as historical fundamental data). Furthermore, many of these academic T 9 P1: JYS c02 JWBK321-Chan September 24, 2008 13:47 Printer: Yet to come 10 QUANTITATIVE TRADING TABLE 2.1 Sources of Trading Ideas Type Academic Business schools’ ﬁnance professors’ web sites Social Science Research Network National Bureau of Economic Research Business schools’ quantitative ﬁnance seminars Mark Hulbert’s column in the New York Times’ Sunday business section Buttonwood column in the Economist magazine’s ﬁnance section URL www.hbs.edu/research/research .html www.ssrn.com www.nber.org www.ieor.columbia.edu/seminars/ ﬁnancialengineering www.nytimes.com www.economist.com Financial web sites and blogs Yahoo!

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The Quants
** by
Scott Patterson

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Albert Einstein, asset allocation, automated trading system, beat the dealer, 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, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, 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, job automation, John Meriwether, John Nash: game theory, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, 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

Weinstein left Deutsche Bank on February 5, slightly more than a decade after he’d first come to the firm as a starry-eyed twenty-four-year-old with dreams of making a fortune on Wall Street. He’d made his fortune, but he’d been bruised and bloodied in one of the greatest market routs of all time. Paul Wilmott stood before a crowded room in the Renaissance Hotel in midtown Manhattan, holding up a sheet of paper peppered with obscure mathematical notations. The founder of Oxford University’s first program in quantitative finance, as well as creator of the Certificate in Quantitative Finance program, the first international course on financial engineering, wrinkled his nose. “There are a lot of people making things far more complicated than they should be,” he said, shaking the paper with something close to anger. “And that’s a guaranteed way to lose $2 trillion.” He paused for a second and snickered, running a hand through his rumpled mop of light brown hair.

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The goal was to become a piranha, gobbling up the fleeting inefficiencies, the hidden discrepancies, as quickly as possible. The quants with the best models and fastest computers win the game. Crucially, EMH gave the quants a touchstone for what the market should look like if it were perfectly efficient, constantly gravitating toward equilibrium. In other words, it gave them a reflection of the Truth, the holy grail of quantitative finance, explaining how the market worked and how to measure it. Every time prices in the market deviated from the Truth, computerized quant piranhas would detect the error, swoop in, and restore order—collecting a healthy profit along the way. Their high-powered computers would comb through global markets like Truth-seeking radar, searching for opportunities. The quants’ models could discover when prices deviated from equilibrium.

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In 1992, soon after Asness arrived on the scene, Fama and French published their most important breakthrough yet, a paper that stands as arguably the most important academic finance research of the last two decades. And the ambition behind it was immense: to overturn the bedrock theory of finance itself, the capital asset pricing model, otherwise known as CAPM. Before Fama and French, CAPM was the closest approximation to the Truth in quantitative finance. According to the grandfather of CAPM, William Sharpe, the most important element in determining a stock’s potential future return is its beta, a measure of how volatile the stock is compared with the rest of the market. And according to CAPM, the riskier the stock, the higher the potential reward. The upshot: long-term investments in risky stocks tended to pay off more than investments in the ho-hum blue chips.

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

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

Cold fusion and stem cell cloning didn’t survive long. But quantitative finance is also subject to a discipline—the discipline of making money. If your idea is wrong or not implemented properly, a lot of money, not to mention your job, may be lost. The old jibe “if you’re so smart, why aren’t you rich?” is appropriate in a financial institution. Some quants overcome dysfunctional attitudes learned in academia, play JWPR007-Lindsey 238 May 7, 2007 17:9 h ow i b e cam e a quant important roles in their businesses, and become rich. Others relegate themselves to the back room forever. The Art of Leaving Things Out Quantitative finance is a craft and a trade. It is barely engineering, and it is certainly not a science. Good quantitative finance can be summed up as the art of leaving things out, plus the art of selecting the right tools.

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It was a golden age of physics: full of interesting and important problems, and only a few people with the skill and training to solve them. Although I missed that revolutionary period in physics, I’ve been fortunate to participate in another period of intellectual fervor: the revolution of quantitative finance. Here too, a series of intellectual breakthroughs, and the subsequent development of tools, techniques, and practical models, have led to fundamental changes in our understanding 29 JWPR007-Lindsey 30 May 7, 2007 16:30 h ow i b e cam e a quant of a field, with broad-ranging and important implications. If quantum mechanics occupies a loftier spot in human intellectual history, the quantitative finance revolution has significantly affected people’s lives and involved exciting challenges for the participants. My career in finance has concentrated on investing. The art of investing is evolving into the science of investing, and I have been fortunate to participate in some of the revolutionary changes that have underpinned that evolution.

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Real scientists never estimate anything without simultaneously computing confidence bounds. Much stupidity in quantitative finance is due to failure to specify the uncertainty associated with estimates of financial quantities. With modern fast computers, there is no excuse for this; it is simple to create error bounds by JWPR007-Lindsey 240 May 7, 2007 17:9 h ow i b e cam e a quant resampling from the original data with replacement. If quants only knew how fuzzy many of their inputs are, they wouldn’t waste so much time on ultra-refined high-precision models. But then again, perhaps they would, because ultra-refined high-precision models are what they are good at. If all you’ve got is a hammer . . . 3. Copulas. Copulas provide an example of the haphazard evolution of quantitative finance. The key result is Sklar’s theorem, which says that one can characterize any multivariate probability distribution by its copula (which specifies the correlation structure) and its marginal distributions (the conditional one dimensional distributions).

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Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives
** by
Satyajit Das

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accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business process, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial innovation, fixed income, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Meriwether, locking in a profit, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, purchasing power parity, quantitative trading / quantitative ﬁnance, random walk, regulatory arbitrage, Right to Buy, risk-adjusted returns, risk/return, Satyajit Das, shareholder value, short selling, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

One quant apparently embedded code in his computer programs to monitor usage and used the volume of usage to establish his value and contribution. Some quants just gave up and moved to trading entirely. Many of the LTCM principals took this career path. Today, rather than firms taking scientists and retooling them in the vagaries of markets, quantitative finance graduates are hired. Their training combines finance and mathematics used in trading. Some postgraduate programmes allow scientists to reinvent themselves for a career in finance. This is the quants conveyor belt. Supply far exceeds demand – every university has established quantitative finance programmes. There is huge demand from people who see these programmes as an entry into a high-paying career in finance. In countries where the university system is state operated, the programmes have a different agenda: the institution can charge full fees, helping to fund it and cross-subsidizing the programmes on Ancient Greek.

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Be afraid.” TELLS A CRACKING STORY, AND IN DOING SO REVEALS BOTH THE HARD FACTS AND THE SOFT UNDERBELLY OF THE WORLD OF DERIVATIVES Frank Partnoy, author of FIASCO and Infectious Greed and Professor of Law at the University of San Diego “Very, very real. Risk management at the front line, and unlike anything you’ll have seen in the textbooks.” Paul Wilmott, writer, mathematician and author of the quantitative ﬁnance website www.wilmott.com Warren Buffet once memorably described derivatives as “ﬁnancial weapons of mass destruction”. Read this sensational and controversial account of the often dazzling business of derivatives trading, and see if you agree. Das is the author of a number of highly regarded standard reference books on derivatives including Swaps/Financial Derivatives (2004, Wiley), Structured Products & Hybrid Securities (2001, Wiley) and Credit Derivatives, CDOs and Structured Credit Products (2005, Wiley).

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Portfolio managers speak of my fund and owning Microsoft. The money isn’t actually theirs. For dealers, they are important clients. Dealers lavish goodies on fund managers – opening nights, polo lessons, cases of vintage champagne, retreats at Swiss ski resorts. Fund managers throw the weight of their money around. The quantitative part comes from the fact that many fund managers are actually defrocked actuaries. Early quantitative finance also focused on investment. There are, in reality, only a few key principles for fund management: 1 Diversification – Harry Markowitz ‘proved’ that putting all your own eggs in one basket was risky. Warren Buffet continues to defy this DAS_C04.QXP 8/7/06 8:39 PM Page 111 3 N Tr u e l i e s – t h e ‘ b u y ’ s i d e 111 successfully. He argues that you are better off putting your money into a few things you know and understand, and that are cheap.

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High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
** by
Irene Aldridge

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algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, trade route, transaction costs, value at risk, yield curve, zero-sum game

“Limit versus Market Orders.” Working paper, Georgetown University. Artzner, P., F. Delbaen, J. Eber and D. Heath, 1997. “Thinking Coherently.” Risk 10(11), 68–71. Atiase, R. 1985. “Predisclosure Information, Firm Capitalization and Security Price Behavior around Earnings Announcements.” Journal of Accounting Research 21–36. Avellaneda, Marco and Sasha Stoikov, 2008. “High-Frequency Trading in a Limit Order Book.” Quantitative Finance, Vol. 8, No. 3., 217–224. Bae, Kee-Hong, Hasung Jang and Kyung Suh Park, 2003. “Traders’ Choice between Limit and Market Orders: Evidence from NYSE Stocks.” Journal of Financial Markets 6, 517–538. Bagehot, W., (pseud.) 1971. “The Only Game in Town.” Financial Analysts Journal 27, 12–14, 22. Bailey, W., 1990. “US Money Supply Announcements and Pacific Rim Stock Markets: Evidence and Implications.”

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“Co-Integration and Error-Correction: Representation, Estimation, and Testing.” Econometrica 35, 251–276. Engle, R.F. and J. Lange, 2001. “Predicting VNET: A Model of the Dynamics of Market Depth.” Journal of Financial Markets 4, 113–142. Engle, R.F. and A. Lunde, 2003. “Trades and Quotes: A Bivariate Point Process.” Journal of Financial Econometrics 1, 159–188. Engle, R.F. and A.J. Patton, 2001. “What Good is a Volatility Model?” Quantitative Finance 1, 237–245. Engle, R.F. and J.R. Russell, 1997. “Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Conditional Duration Model.” Journal of Empirical Finance 4, 187–212. Engle, R.F. and J.R. Russell, 1998. “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transactions Data.” Econometrica 66, 1127–1162. Errunza, V. and K. Hogan, 1998.

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The second part reviews the statistical and econometric foundations of the common types of 6 HIGH-FREQUENCY TRADING high-frequency strategies. The third part addresses the details of modeling high-frequency trading strategies. The fourth part describes the steps required to build a quality high-frequency trading system. The fifth and last part addresses the issues of running, monitoring, and benchmarking highfrequency trading systems. The book includes numerous quantitative trading strategies with references to the studies that first documented the ideas. The trading strategies discussed illustrate practical considerations behind high-frequency trading. Chapter 10 considers strategies of the highest frequency, with position-holding periods of one minute or less. Chapter 11 looks into a class of high-frequency strategies known as the market microstructure models, with typical holding periods seldom exceeding 10 minutes.

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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, creative destruction, 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, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, 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, Metcalfe’s law, 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, 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, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, 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, your tax dollars at work

Many of his most insightful ideas are here in this book, the utility of which are only matched by the humor of their presentation. As the markets have changed in 2008, the need to collect, process, and understand novel information sources has never been greater.” Jacob Sisk Quantextual Nerd Extraordinaire, Infoshock,Yahoo! “Who says there is neither wit nor wisdom on Wall Street? This account of the evolution of quantitative finance is an invaluable guide for anyone seeking to understand everything from how indexed investing works to the nature of that elusive concept, ‘alpha’. The accessible style and deadpan humor make this a book that even those with an advanced case of fear of mathematical formulae can understand and enjoy.” Suzanne McGee, Journalist, Wall Street Journal & Barrons “Thoughtful insights covering trading, investment practice and system design encased in humor by an expert in all four: a good and practical read.”

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Thus was born the PDP-1, as well as its successors, up to the PDP-10, like the one at Harvard’s Aiken Comp Lab used by a sophomore named Gates to write the first Microsoft product in 1973. Today, almost all teenage nerds have more computational gear than they know what to do with. Back then, in the 1970s, access to a machine like the PDP-1, with graphics, sound, plotting, and a supportive hacker 3 culture, was a rare opportunity. It was also the first of the series of accidents that eventually led me into quantitative finance. I wish I could say that I realized the PDP-1 would allow me to use the insights of Fischer Black, Myron Scholes, and Robert Merton to become a god of the options market and buy Chicago, but those were the guys at O’Connor & Associates and Chicago Research and Trading, not me. I used the machine to simulate nuclear physics experiments for the lab that adopted me as a sophomore. They flew me down to use the particle Intr oduction xix accelerators at Brookhaven National Laboratory to find out the meaning of life, the universe, and everything by smashing one atomic nucleus into another—sort of a demolition derby with protons.

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If perchance any reader has one, please send me a copy.5 Now we return to the plotline of how I became a quant. At RAND, I started out doing nice civilian work, artificial intelligence (AI)–inspired analysis of econometric models for the Department of Energy (DoE) and the Environmental Protection Agency (EPA), helping with the design of a storm surge barrier for the Dutch water ministry. It was all very interesting, but fairly remote from quantitative finance. In 1980, Ronald Reagan won the election, promising to abolish both the EPA and the DoE. He didn’t quite do that, but the cash flow to RAND from those agencies slowed to a trickle. The Dutch stopped analyzing and started building the Oosterschelde storm surge barrier.6 I was drafted into the military side of RAND. There were classified and unclassified sides of the building, separated by thick, secure glass doors operated by guards.

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Red-Blooded Risk: The Secret History of Wall Street
** by
Aaron Brown,
Eric Kim

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activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, financial innovation, illegal immigration, implied volatility, index fund, Long Term Capital Management, loss aversion, margin call, market clearing, market fundamentalism, market microstructure, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, pre–internet, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

Her eyes widened as she exclaimed, “I have BOXES of them in my attic.” The standard introduction to quantitative finance is Paul Wilmott Introduces Quantitative Finance by, of course, Paul Wilmott. A few years ago Paul told me he had ceased being a person and had transformed into a brand. Antti Ilmanen wrote an excellent guide to the theory and practice of quant strategies, Expected Returns: An Investor’s Guide to Harvesting Market Rewards. More technical works on the subject are Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson, Inside the Black Box: The Simple Truth about Quantitative Trading by Rishi K. Narang, and Multifractal Volatility: Theory, Forecasting, and Pricing by Laurent E. Calvet. The view of quantitative finance described in Red-Blooded Risk has a lot of overlap with two pathbreaking but eccentric works: The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage by Ralph Vince and Finding Alpha: The Search for Alpha When Risk and Return Break Down by Eric Falkenstein.

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A more famous pathbreaking and eccentric work is Benoit Mandelbrot’s The (Mis)behavior of Markets. Two of the best books on the future of finance are The New Financial Order: Risk in the 21st Century by Robert J. Shiller and Financing the Future: Market-Based Innovations for Growth by Franklin Allen and Glenn Yago. Both cover quite a bit of history to ground their predictions in something solid. If you like to study your quantitative finance through people, Espen Haug’s Derivatives Models on Models is an excellent choice. Also consider The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It by Scott Patterson, My Life as a Quant: Reflections on Physics and Finance by Emanuel Derman, Inside the House of Money: Top Hedge Fund Traders on Profiting in the Global Markets by Steven Drobny, and More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby.

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See also Change of numeraire definition derivative money diversity of economic value and market return and money as O’Brien, Tim Off-balance-sheet items On Being Certain (Burton) Opportunity, danger and Options, smile and skew Origins of the Crash (Lowenstein) Osband, Kent Pairs trading Paleonomics Paper money: clearing mechanisms end of for loans government and vs. precious metal replacement for Parametric VaR Park, Tim Pascal, Blaise: bets, dice throws and letters to Fermat Pascal’s wager religion, risk and utility maximization and Wall Street and Patterson, Scott Paul Wilmott Introduces Quantitative Finance (Wilmott) Performance ratio Perrow, Charles Petroski, Henry Pfizer Philosophical Lectures on Probability (de Finetti) Pilkey, Orrin Play Plight of the Fortunetellers (Rebonato) Poisson, Siméon Poker Poker player types Polanyi, Karl Politics: election polling religion and Politics of Large Numbers, The (Desrosières) Porter, Theodore Portfolio Risk Analysis (Connor, Goldberg, and Korajczyk) Portfolio Selection (Markowitz) Poundstone, William Predictably Irrational (Ariely) Price discovery Probability.

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Data Scientists at Work
** by
Sebastian Gutierrez

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Albert Einstein, algorithmic trading, Bayesian statistics, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, commoditize, computer vision, continuous integration, correlation does not imply causation, creative destruction, crowdsourcing, data is the new oil, DevOps, domain-specific language, Donald Knuth, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, Intergovernmental Panel on Climate Change (IPCC), inventory management, iterative process, lifelogging, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, 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

After I left academics, I worked for an enjoyable couple of years as an assistant editor for the Physical Review in Brookhaven. It was very interesting to see how the review and referee process works, and how the sausage is made in the academic publishing world. It set me to thinking a lot more about computation and data. And I was in the vicinity of New York and the financial industry. Going to work in the quantitative finance industry was definitely my first jump into a very data-intensive world. Quantitative finance broadly divides into two fields. In the first field, people work with very complicated financial instruments, such as over-the-counter derivatives, which are traded very simply, usually over the phone. There are mathematical frameworks around these complicated instruments, which have theoretical, analytical, and computational aspects. The second field is high-frequency algorithmic trading, in which you have very simple financial instruments, such as equities, which are traded in very complicated ways.

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If I have to explain probability to someone, it’s going to be a really hard slog for everyone involved. I would rather take someone in the top 20 percent of quantitative skills who also is a great software engineer over someone in the top 5 percent who doesn’t know how to code. The quantitative finance model really popularized the notion that raw cognitive talent is all that matters. This is the D. E. Shaw and Renaissance Technologies model of “We’re going to take people who have www.it-ebooks.info Data Scientists at Work been doing algebraic topology for a long time, and we’re going to then teach them quantitative finance, and this is going to be a good scheme.” In some sense, it obviously worked out very well for them, but especially on the data side, data analysis is so much messier than actual math. I have friends who work on these topology-based approaches, and I’m like, “You realize these manifolds totally evaporate when you actually throw noise into the system.

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After taking his PhD in physics from Yale, he conducted research on the statistical properties of strongly interacting quark-gluon plasma at Brookhaven National Laboratory, the Niels Bohr Institute, and the University of Virginia. He served as an editor of two journals of the American Physical Society: Physical Review C (nuclear physics) and Physical Review D (particle physics and cosmology). Lenaghan stands out as a prime example of a data scientist who has migrated from physical science to data science via quantitative finance. This richly varied background informs Lenaghan’s nuanced appreciation of the risk dimensions of data science, his optimistic pragmatism, and his conviction that useful data science depends critically on a sound engineering foundation. www.it-ebooks.info 180 Chapter 9 | Jonathan Lenaghan, PlaceIQ Sebastian Gutierrez: Tell me about your journey to becoming a data scientist at PlaceIQ. Jonathan Lenaghan: Prior to joining PlaceIQ as a data scientist in March of 2012, I worked in the financial services industry doing algorithmic trading.

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Frequently Asked Questions in Quantitative Finance
** by
Paul Wilmott

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Albert Einstein, asset allocation, beat the dealer, Black-Scholes formula, Brownian motion, butterfly effect, capital asset pricing model, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discrete time, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, iterative process, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, quantitative trading / quantitative ﬁnance, random walk, regulatory arbitrage, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, transaction costs, urban planning, value at risk, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

His academic and practitioner credentials are impeccable, having written over 100 research papers on mathematics and finance, and having been a partner in a highly profitable volatility arbitrage hedge fund. Dr Wilmott is a consultant, publisher, author and trainer, the proprietor of wilmott.com and the founder of the Certificate in Quantitative Finance (7city.com/cqf). He is the Editor in Chief of the bimonthly quant magazine Wilmott and the author of the student text Paul Wilmott Introduces Quantitative Finance, which covers classical quant finance from the ground up, and Paul Wilmott on Quantitative Finance, the three-volume research-level epic. Both are also published by John Wiley & Sons. Chapter 1 The Quantitative Finance Timeline There follows a speedy, roller-coaster of a ride through the history of quantitative finance, passing through both the highs and lows. Where possible I give dates, name names and refer to the original sources.1 1827 Brown The Scottish botanist, Robert Brown, gave his name to the random motion of small particles in a liquid.

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Journal of Financial Economics 5 177-188 Wiener, N 1923 Differential space. J. Math. and Phys. 58 131-74 Chapter 2 FAQs What are the Different Types of Mathematics Found in Quantitative Finance? Short Answer The fields of mathematics most used in quantitative finance are those of probability theory and differential equations. And, of course, numerical methods are usually needed for producing numbers. Example The classical model for option pricing can be written as a partial differential equation. But the same model also has a probabilistic interpretation in terms of expectations. Long Answer The real-world subject of quantitative finance uses tools from many branches of mathematics. And financial modelling can be approached in a variety of different ways. For some strange reason the advocates of different branches of mathematics get quite emotional when discussing the merits and demerits of their methodologies and those of their ‘opponents.’

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The idea is that solutions to some difficult problems can be built up from solutions to special solutions of a similar problem. References and Further Reading Joshi, M 2003 The Concepts and Practice of Mathematical Finance. CUP Wilmott, P 2001 Paul Wilmott Introduces Quantitative Finance, second edition. John Wiley & Sons Wilmott, P 2006 Paul Wilmott On Quantitative Finance, second edition. John Wiley & Sons What is Arbitrage? Short Answer Arbitrage is making a sure profit in excess of the risk-free rate of return. In the language of quantitative finance we can say an arbitrage opportunity is a portfolio of zero value today which is of positive value in the future with positive probability and of negative value in the future with zero probability. The assumption that there are no arbitrage opportunities in the market is fundamental to classical finance theory.

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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, computerized trading, corporate raider, 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, John Meriwether, Kenneth Rogoff, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, Paul Samuelson, 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, survivorship bias, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs

THE CODE BREAKERS 14. PREMONITIONS OF A CRISIS 15. RIDING THE STORM 16. “HOW COULD THEY DO THIS?” CONCLUSION: SCARIER THAN WHAT? Acknowledgments Appendix I: Do the Tiger Funds Generate Alpha? Appendix II: Performance of the Pioneers Notes Photo Credits INTRODUCTION: THE ALPHA GAME The first hedge-fund manager, Alfred Winslow Jones, did not go to business school. He did not possess a PhD in quantitative finance. He did not spend his formative years at Morgan Stanley, Goldman Sachs, or any other incubator for masters of the universe. Instead, he took a job on a tramp steamer, studied at the Marxist Workers School in Berlin, and ran secret missions for a clandestine anti-Nazi group called the Leninist Organization. He married, divorced, and married again, honeymooning on the front lines of the civil war in Spain, traveling and drinking with Dorothy Parker and Ernest Hemingway.

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It was only at the advanced age of forty-eight that Jones raked together $100,000 to set up a “hedged fund,” generating extraordinary profits through the 1950s and 1960s. Almost by accident, Jones improvised an investment structure that has endured to this day. It will thrive for years to come, despite a cacophony of naysayers. Half a century after Jones created his hedge fund, a young man named Clifford Asness followed in his footsteps. Asness did attend a business school. He did acquire a PhD in quantitative finance. He did work for Goldman Sachs, and he was a master of the universe. Whereas Jones had launched his venture in his mature, starched-collar years, Asness rushed into the business at the grand old age of thirty-one, beating all records for a new start-up by raising an eye-popping $1 billion. Whereas Jones had been discreet about his methods and the riches that they brought, Asness was refreshingly open, tearing up his schedule to do TV interviews and confessing to the New York Times that “it doesn’t suck” to be worth millions.1 By the eve of the subprime mortgage crash in 2007, Asness’s firm, AQR Capital Management, was running a remarkable $38 billion and Asness himself personified the new globe-changing finance.

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LTCM bought CCTs and shorted BOTs, betting on their convergence. See LTCM, “Portfolio Outline.” 13. Ibid. 14. Andre Perold, “Long-Term Capital Management, L.P. (A)” (Harvard Business School case study 9-200-007, November 5, 1999). 15. Lowenstein, When Genius Failed, p. 90. 16. Ibid., p. 84. 17. What follows on risk management is drawn partly from Eric Rosenfeld’s draft article for the Encyclopedia of Quantitative Finance, ed. Rama Cont (Hoboken, NJ: John Wiley & Sons, 2010). 18. To work out the worst loss on ninety-nine out of a hundred days, LTCM would take the standard deviation of a position, meaning the amount of variation from the mean that occurred in 68 percent of cases, and multiply by 2.58 to get the variation from the mean that occurred in 99 percent of cases. Thus, a position with a standard deviation of six basis points would not fall by more than about fifteen basis points in 99 percent of cases, or on ninety-nine days out of a hundred. 19.

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Topics in Market Microstructure
** by
Ilija I. Zovko

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Brownian motion, computerized trading, continuous double auction, correlation coefficient, financial intermediation, Gini coefficient, information asymmetry, market design, market friction, market microstructure, Murray Gell-Mann, p-value, quantitative trading / quantitative ﬁnance, random walk, stochastic process, stochastic volatility, transaction costs

E, 62(6):R7615–R7618, Dec 2000. doi: 10.1103/PhysRevE.62.R7615. H. Boswijk, C. H. Hommes, and S. Manzan. Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control, 31 (6):1938–1970, 2007. J.-P. Bouchaud, M. Mezard, and M. Potters. Statistical properties of the stock order books: empirical results and models. Quantitative Finance, 2(4):251–256, 2002. J.-P. Bouchaud, Y. Gefen, M. Potters, and M. Wyart. Fluctuations and response in financial markets: The subtle nature of “random” price changes. Quantitative Finance, 4(2):176–190, 2004. W. A. Brock and B. LeBaron. A dynamic structural model for stock return volatility and trading volume. The Review of Econoics and Statistics, 78(1):94–110, 1996. J. Carlson and M. Lo. One minute in the life of the dm/usd: Public news in an electronic market. Journal of International Money and Finance, Jan 2006.

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A growing number of econophysics papers are now being published in more-and-more mainstream economics and finance journals. Chapters 2 and 3 of this thesis are based on two papers§† and are related to the literature dealing with the microstructure of limit order books. Chapter 4 is based on a paper‡ related to the litera§ I. I. Zovko and J. D. Farmer. The power of patience; a behavioral regularity in limit order placement. Quantitative Finance, 2(5):387392, 2002. † J. D. Farmer, P. Patelli, and I. Zovko. The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences of the United States of America, 102(6):22549, Feb 8 2005. ‡ I. I. Zovko and J. D. Farmer, Correlations and clustering in the trading of members of the London Stock Exchange, in Complexity, Metastability and Nonextensivity: An International Conference, S.

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The heterogeneity of order sizes present at the market seems to be a consequence of the fat-tailed distribution of order sizes: for the onbook market with a tail exponent equal to 3, for the off-book market equal to 3/2 (tail exponents are for the cumulative distribution). 7 Chapter 2 The power of patience: A behavioral regularity in limit order placement Published as Ilija I. Zovko, J. Doyne Farmer, The power of patience: A behavioral regularity in limit order placement, Quantitative Finance, 2002, No 5, Volume 2, pp: 387-392. 2.1 Introduction Most modern financial markets are designed as a complex hybrid composed of a continuous double auction and an ’upstairs’ trading mechanism serving the purpose of block trades. The double auction is believed to be the primary price discovery mechanism1 . Limit orders, which specify both a quantity and a limit price (the worst acceptable price), are the liquidity providing mechanism for double auctions and the proper understanding of their submission process is important in the study of price formation. 1 According to the London Stock Exchange information bulletins (“SETS four years on - October 2001”, published by the London Stock Exchange), since the introduction of the SETS in 1997 to October 2001, the average percentage of trades in order book securities that have been executed at the price shown on the order book is 70% - 75%.

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My Life as a Quant: Reflections on Physics and Finance
** by
Emanuel Derman

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Berlin Wall, bioinformatics, Black-Scholes formula, Brownian motion, capital asset pricing model, Claude Shannon: information theory, Donald Knuth, Emanuel Derman, fixed income, Gödel, Escher, Bach, haute couture, hiring and firing, implied volatility, interest rate derivative, Jeff Bezos, John Meriwether, John von Neumann, law of one price, linked data, Long Term Capital Management, moral hazard, Murray Gell-Mann, Myron Scholes, Paul Samuelson, pre–internet, publish or perish, quantitative trading / quantitative ﬁnance, Richard Feynman, Sharpe ratio, statistical arbitrage, statistical model, Stephen Hawking, Steve Jobs, stochastic volatility, technology bubble, the new new thing, transaction costs, value at risk, volatility smile, Y2K, yield curve, zero-coupon bond, zero-sum game

Formerly called "rocket scientists" by those who mistakenly thought that rocketry was the most advanced branch of science, they are now commonly called "quants." Quants and their cohorts practice "financial engineering"-an awkward neologism coined to describe the jumble of activities that would better be termed quantitative finance. The subject is an interdisciplinary mix of physics-inspired models, mathematical techniques, and computer science, all aimed at the valuation of financial securities. The best quantitative finance brings real insight into the relation between value and uncertainty, and it approaches the quality of real science; the worst is a pseudoscientific hodgepodge of complex mathematics used with obscure justification. Until recently, financial engineering wasn't really a subject at allwhen I entered the field in 1985, it didn't have a name and was something one learned on the job at an investment bank.

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Phenomenologists elaborate the theory; they create heuristic approximations to engineer the theory into a pragmatic tool; they propose experiments to validate or refute a theory, using the theory itself to compute the expected results. Phenomenologists deal a little more with the ripples on the surface and a little less with the laws beneath it. Though I wanted to do pure theory, I ultimately ended up spending much of my life in physics as a phenomenologist. Over the long run, this stood me in very good stead. When I moved to Will Street, I found quantitative finance to resemble phenomenology much more than it resembled pure theory. Quantitative finance is concerned with the techniques that people use to value financial contracts and, given the fluctuations of the human psyche, it is a pragmatic study of surfaces rather than a principled study of depths. Physics, in contrast, is concerned with God's canons, which seem to be more easily captured in the simple broad statements that characterize profound physical laws.

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If you had asked me where quantitative finance was headed, I would have hoped for the discovery of that theory. Seventeen years on, I say without regret that things aren't the way I expected. There is no unified theory. Models must necessarily be pragmatic, and traders typically use a variety of similar but slightly inconsistent models-one for Treasury bonds, another for corporates, a third for caps, a fourth for swaptions-even though all these securities depend on the same underlying interest rates. Though we aspire to it, we don't expect comprehensiveness. The best quants know that it is unattainable. Newcomers to the field find it hard to swallow. One French student in my course recently wrote in his evaluation that, although the course gave him a good feel for the way quantitative finance is practiced, he is "still not convinced that an ab initio model in finance (like the sophisticated ones in other fields) to explain almost everything does not exist" Physicists new to finance, as I did, imagine a grand unified theory can be found.

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The Misbehavior of Markets
** by
Benoit Mandelbrot

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Albert Einstein, asset allocation, Augustin-Louis Cauchy, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black-Scholes formula, British Empire, Brownian motion, buy low sell high, capital asset pricing model, carbon-based life, discounted cash flows, diversification, double helix, Edward Lorenz: Chaos theory, Elliott wave, equity premium, Eugene Fama: efficient market hypothesis, Fellow of the Royal Society, full employment, Georg Cantor, Henri Poincaré, implied volatility, index fund, informal economy, invisible hand, John Meriwether, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market microstructure, Myron Scholes, new economy, paper trading, passive investing, Paul Lévy, Paul Samuelson, Plutocrats, plutocrats, price mechanism, quantitative trading / quantitative ﬁnance, Ralph Nelson Elliott, RAND corporation, random walk, risk tolerance, Robert Shiller, Robert Shiller, short selling, statistical arbitrage, statistical model, Steve Ballmer, stochastic volatility, transfer pricing, value at risk, Vilfredo Pareto, volatility smile

Scaling in financial prices, I: Tails and dependence. Quantitative Finance 1: 113-123. • Reprint: Beyond Efficiency and Equilibrium. Edited by Doyne Farmer & John Geanakoplos, Oxford UK: The University Press, 2004. Mandelbrot, Benoit B. 2001b. Scaling in financial prices, II: Multifractals and the star equation. Quantitative Finance 1: 124-130. • Reprint: Beyond Efficiency and Equilibrium. Edited by Doyne Farmer and John Geanakoplos. Oxford, UK: The University Press, 2004. Mandelbrot, Benoit B. 2001c. Scaling in financial prices, III: Cartoon Brownian motions in multifractal time. Quantitative Finance 1: 427-440. Mandelbrot, Benoit B. 2001d. Scaling in financial prices, IV: Multifractal concentration. Quantitative Finance 1: 641-649. Mandelbrot, Benoit B. 2001e. Stochastic volatility, power-laws and long memory. Quantitative Finance 1: 558-559.

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The first on “long-term dependence” and the Hurst Effect were Mandelbrot 1965a and Mandelbrot and Van Ness 1968. The first on financial “bubbles” was Mandelbrot 1966a; on “trading time” and “subordination,” Mandelbrot and Taylor 1967; on “multifractals,” pointing out their possible applications to economics, Mandelbrot 1972. xxiii “That record, alone…” A summary of Mandelbrot’s most recent work in finance may be found in a four-part series that appeared in Quantitative Finance (Mandelbrot 2001a-d). His most important past writings are being republished with extensive comments. So far, four volumes have appeared: Mandelbrot 1997a, 1999a, cover the topics suggested by their titles. The title of Mandelbrot 2002 is less descriptive therefore the contents of several chapters deserve to be singled out. Chapter H0, an overview of fractals and multifractals, is of wide general interest.

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Taming large events: Optimal portfolio theory for strongly fluctuating assets. International Journal of Theoretical and Applied Finance 1 (1): 25-41. Bouchaud, Jean-Philippe and Marc Potters. 2000. Theory of Financial Risks: From Statistical Physics to Risk Management. Cambridge, U.K.: Cambridge University Press. Bouchaud, Jean-Philippe and Marc Potters. 2001. Welcome to a non-Black-Scholes world. Quantitative Finance 1 (5): 482-483. Bouchaud, Jean-Philippe. 2002. An introduction to statistical finance. Physica A 313: 238-251. Buffett, Warren E. 1988. To the Shareholders of Berkshire Hathaway Inc. Annual Report. Omaha, Neb.: Berkshire Hathaway Inc. Burton, Jonathan. 1998. Revisiting the capital asset pricing model. Dow Jones Asset Manager May-June: 20-28. Calvet, Laurent, Adlai Fisher, and Benoit Mandelbrot. 1997.

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Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues
** by
Alain Ruttiens

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algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, discrete time, diversification, fixed income, implied volatility, interest rate derivative, interest rate swap, margin call, market microstructure, martingale, p-value, passive investing, quantitative trading / quantitative ﬁnance, random walk, risk/return, Satyajit Das, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

Didier Marteau, without whom this book would not exist Foreword The valuation and risk dimensions of financial instruments, and, to some extent, the way they behave, rest on a vast, complex set of mathematical models grouped into what is called quantitative finance. Today more than ever, it should be required that each and every one involved in financial markets or products has good command of quantitative finance. The problem is that the many books in this field are devoted either to a specific type of financial instruments, combining product description and quantitative aspects, or to a specific mathematical or statistical theory, or otherwise, with an impressive degree of mathematical formalism, which needs a high degree of competence in mathematics and quantitative methods. Alain Ruttiens' text is aiming to offer in a single book what should be needed to be known by a wide readership to master the quantitative finance at large. It covers, on the one hand, all the financial products, from the traditional spot instruments in forex, stocks, interest rates, and so on, to the most complex derivatives, and, on the other hand, the major quantitative tools designed to value them, and to assess their risk potentials.

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Tze Leung LAI, Haipeng XING, Statistical Models and Methods for Financial Markets, Springer, 2008, 374 p. David RUPPERT, Statistics and Finance, An Introduction, Springer, 2004, 482 p. Dan STEFANICA, A Primer for the Mathematics of Financial Engineering, FE Press, 2011, 352 p. Robert STEINER, Mastering Financial Calculations, FT Prentice Hall, 1997, 400 p. John L. TEALL, Financial Market Analytics, Quorum Books, 1999, 328 p. Presents the maths needed to understand quantitative finance, with examples and applications focusing on financial markets. 1. (Translated from French) Thomas RIEN, Cette mémoire du cœur, 1985. 2. Who sadly passed away recently. Part I The Deterministic Environment 1 Prior to the yield curve: spot and forward rates 1.1 INTEREST RATES, PRESENT AND FUTURE VALUES, INTEREST COMPOUNDING Consider a period of time, from t0 to t, in Figure 1.1.

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This relationship can be re-written as (8.13) Having defined the return of S between t − 1 and t under its usual form by (St − St−1)/St−1, or St/St−1 − 1, we can see that the ln of this expression gives a nearby result and can also be considered as a “return”, more specifically called the ln of the return or the “log return”, that is, Example. With St−1 = 100 and St = 101, the classical return is 0.01 or 1% and the log return is ln 101/100 = 0.00995. Actually, log returns are extensively used in quantitative finance, due to the pertinence and usefulness of Eq. 8.13. Equation 8.13 thus models the log returns of S(t), that are normally distributed (because of the term in Z(t)). Furthermore, from Eq. 8.13 we can model the prices themselves: (8.14) meaning that while the log returns of S(t) are normally distributed, the prices themselves are “log-normally” distributed (the log-normal distribution is defined as the probability distribution of a variable of which the distribution of its ln is normal).

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

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activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, bank run, beat the dealer, Benoit Mandelbrot, Black-Scholes formula, Bretton Woods, Brownian motion, capital asset pricing model, card file, Cass Sunstein, collateralized debt obligation, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discovery of the americas, diversification, diversified portfolio, Edward Glaeser, Edward Thorp, endowment effect, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market design, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, prediction markets, pushing on a string, quantitative trading / quantitative ﬁnance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, South Sea Bubble, statistical model, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

He found something far more interesting: Bachelier’s 1900 doctoral dissertation, the Théorie de la spéculation. Samuelson recognized almost immediately that Bachelier’s densely mathematical description of market behavior was almost identical to Albert Einstein’s description of Brownian motion—the random movement of microscopic particles suspended in a liquid or gas. The significance of this discovery to the subsequent development of quantitative finance cannot be overstated. Economists and finance professors could claim that one of their own—and they embraced the deceased French mathematician as such—had beaten the great Einstein to a major discovery. Samuelson’s first order of business after encountering Bachelier’s work was to help his student incorporate the parts on options into his almost-finished dissertation. He then began thinking about whether Bachelier’s formula actually fit real-life security markets.

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And it was Fama who determined that if his efficient market was to have any real meaning, it needed to be joined at the hip to CAPM. Fama did the joining at the 1969 annual meeting of the American Finance Association. As published in the Journal of Finance the next year under the title “Efficient Markets: Theory and Evidence,” his paper became—along with Harry Markowitz’s portfolio theory, the M&M propositions, and the several CAPM papers—a core document of the new quantitative finance. Fama was trying to lend rigor to an enterprise that up to then had been marked mostly by enthusiasm. To argue that the market was hard to outsmart was one thing; to argue that it was right was another. Fama wished to assert that the market got prices right: The primary role of the capital market is allocation of ownership of the economy’s capital stock. In general terms, the ideal is a market in which prices provide accurate signals for resource allocation: that is, a market in which firms can make production-investment decisions, and investors can choose among the securities that represent ownership of firms’ activities under the assumption that security prices at any time “fully reflect” all available information.

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In 1957, Jack Bogle had proposed a new performance measure that divided fund return by volatility,24 the better to showcase Wellington’s low-risk approach.25 But most Wall Streeters didn’t think volatility and risk were the same, and Bogle himself eventually stopped trying to fight the “performance” obsession. First, he persuaded his bosses to launch the all-stock Windsor Fund. Then, in 1966, just before taking over as Wellington’s president, he arranged to merge it with a small Boston firm that ran one of the hottest performance funds of the day. This left the field open to the practitioners of the new academic, quantitative finance. First to arrive was Jack Treynor of Arthur D. Little. In the late 1950s, Investors Diversified Services (IDS, now Ameriprise Financial), a Minnesota firm staffed by legions of door-to-door salesmen, passed Massachusetts Investors Trust and sister fund Massachusetts Investors Growth to become the country’s biggest mutual fund complex. IDS wanted advice on what to do with all the money its salespeople were bringing in.

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

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Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, butterfly effect, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, V2 rocket, Vilfredo Pareto, volatility smile

Beat the Dealer: A Winning Strategy for the Game of Twenty One. New York: Vintage Books. — — — . 1984. The Mathematics of Gambling. Secaucus, NJ: Lyle Stuart. — — — . 1998. “The Invention of the First Wearable Computer.” Digest of Papers. Second International Symposium on Wearable Computers, 1998, 4–8. — — — . 2004. “A Perspective on Quantitative Finance: Models for Beating the Market.” In The Best of Wilmott 1: Incorporating the Quantitative Finance Review, ed. Paul Wilmott, 33–38. Hoboken, NJ: John Wiley and Sons. — — — . 2006. “The Kelly Criteria in Blackjack, Sports Betting, and the Stock Market.” In Theory and Methodology, vol. 1 of The Handbook of Asset and Liability Management, ed. S. A. Zenios and W. T. Ziemba. Amsterdam: North Holland. Thorp, Edward O., and Sheen T.

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But unlike Samuelson, Black was able to communicate to investors and Wall Street bankers how the new ideas coming out of physics could be used in practice. Thorp was the first person to figure out how to use Bachelier’s and Osborne’s random walk hypothesis to make a profit, but he did so outside of the establishment, through Princeton-Newport Partners. Black, on the other hand, was the person who made quantitative finance, with its deep roots in physics, an essential part of investment banking. Black took physics to the Street. Black first arrived at Harvard in 1955, at age seventeen. If anyone asked why he applied to Harvard and nowhere else, he would say it was because he liked to sing and Harvard had a great glee club. From the very beginning, he was determined to chart his own course through academia.

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Packard stayed with the company for a few more years, serving as CEO until 2003, when he left to start a new company, called ProtoLife. By the time they left, they had made their point: a firm grasp of statistics and a little creative reappropriation of tools from physics were enough to beat the Man. It was time to tackle a new set of problems. Black box models, and more generally “algorithmic trading,” have taken much of the backlash against quantitative finance in the period since the 2007–2008 financial crisis. The negative press is not undeserved. Black box models often work, but by definition it is impossible to pinpoint why they work, or to fully predict when they are going to fail. This means that black box modelers don’t have the luxury of being able to guess when the assumptions that have gone into their models are going to turn bad. In place of this sort of theoretical backing, the reliability of black box models has to be constantly tested by statistical methods, to determine the extent to which they continue to do what they are intended to do.

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A Primer for the Mathematics of Financial Engineering
** by
Dan Stefanica

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asset allocation, Black-Scholes formula, capital asset pricing model, constrained optimization, delta neutral, discrete time, Emanuel Derman, implied volatility, law of one price, margin call, quantitative trading / quantitative ﬁnance, Sharpe ratio, short selling, time value of money, transaction costs, volatility smile, yield curve, zero-coupon bond

To appear. N [31] Nassim Taleb Dynamic Red' . M . John Wiley &, Sons Ltd, Ne!I~~rk, ~~~~l.ng Vanilla and Exotic Options. [32] Domingo A. Tavella. Quantitative M th d ' '. Introduction to Computational F' e ~ shIn D~nvatIves Pricing: An York, 2002. Inance. 0 n WIley & Sons, Inc., New [33] Paul Wilmot~. Paul Wilmott on Quantitative Finance. Sons Ltd, ChIchester, West Sussex, 2nd edition, 2006. [34] [35] John Wiley & ~r~y~il~~:'Lt~a~h~~lmtott ~~Ttroduces Quantitative Finance. , IC es er, vvest Sussex, 2007. John P~ul Wilmott, Sam Howison, and Jeff De . FInancial Derivatives: A Student Introd W?'nne. The ~athematIcs of Press, New York, 1995. uctlOn. Cambndge University 283 Gamma-neutral, 107 Newton's Method, 248 Gradient, 30, 31 N-dimensional problems, 255 Greeks, 97, 99, 158, 194 Approximate Newton's Method 258 finite difference approximation, 190 Normal random variable , 91 ' probability density, 92 Heat equation, 222 Numerical integration methods, 53 Hedging, 105 Odd function, 3 Hessian, 31 Index Delta-hedging, 105 Delta-neutral, 106, 107 Diagonally dominated matrix, 184 Barrier options, 225 Differentiating integrals Big 0, 12 definite integrals, 25 Bisection Method, 246 improper integrals, 51 Black-Scholes formula, 95, 108, 133, Discount factor, 65, 69 137 Double integral, 45 ATM approximations, 160, 161, 165 Black-Scholes PDE, 191, 193, 194, 222 European options, 34 Bond , 69 , 73 Even function, 1 convexity, 71, 72, 170 Expected value, 81, 84 coupon rate, 69 Extremum points duration, 71, 170 multivariable functions, 214 face value 69 single variable functions, 208 par yield, '70 two variables functions, 209 semiannual coupon bond, 69 Fibonacci sequence, 9 . . 178 yield, 70, 265 Finite difference approxImatIOns, d 72 zero coupon b on, Bootstrapping for zero rate curves, 270 backward finite differences, 178 central finite differences, 178-180 forward finite differences, 178 Call option, 34 Finite difference solutions of ODEs, Chain Rule 182, 185, 188 multivariable functions, 205 Forward contract, 38, 40 single variable functions, 20, 203 Forward price, 38 Characteristic polynomial, 9 Forward rates, 66 Covariance, 86 Fubini's Theorem, 46 Critical point, 208, 210, 215 Fundamental Theorem of Calculus, 22 Cumulative distribution, 84 Futures contract, 38 standard normal, 90, 108 American options, 34 early exercise optimality, 228 Gamma, 97 Delta, 97 call, 98 call, 98, 99 finite difference approximation, 191 finite difference approximation, 191 put, 98 put, 98 282 Implied volatility, 103, 267 approximation formula, 163, 164 Improper integrals, 48 Independent lognormal variables, 126 Independent normal variables, 123 Independent random variables, 121 Instantaneous rate curve, 64 Integration by parts, 22 Integration by substitution, 23 Interest rates, 64 annual compounding, 68 continuous compounding, 64 discrete compounding, 67 instantaneous rates, 64 semiannual compounding, 68 zero rates, 64 Partial derivatives, 29 Plain vanilla European option, 34 Polar coordinates, 207 Portfolio optimization maximal return portfolio, 261 minimal variance portfolio, 261 Power series, 128 radius of convergence, 129 Probability density function, 84 Product Rule, 19 Put option, 34 Put-Call parity, 37, 40, 96, 104 Quotient Rule, 20 Random variable, 81, 84 Rho, 97 call, 98, 102 finite difference approximation, 191 put, 98 Risk-neutral pricing, 133 L'Hopital's Rule, 28 L'Hopital's Rule, 7, 28 Lagrange multipliers, 235 constrained extremum, 236 Law of One Price, 35 Sample space, 84 liminf, 153 Secant Method, 253 limsup, 129 Simpson's Rule, 54, 57, 60 Linear recursions, 8 Standard deviation, 81, 85 Little 0, 13 Standard normal variable, 89 Lognormal model for asset prices, 132 probability density function, 89, 218 Lognormal random variable, 119 Stirling's formula, 131 probability density, 119 Strike price, 34, 94 Maturity, 34, 94 Midpoint Rule, 53, 56, 59 Multivariable functions, 29 scalar valued, 29 vector valued, 31 Taylor approximation linear approximation, 146-151 quadratic approximation, 146-148, 150, 151 Taylor approximation error, 145 284 INDEX derivative form, 144 integral form, 144 Taylor polynomial, 143 Taylor series, 152, 154, 155 radius of convergence, 153, 154 Taylor's formula multivariable functions, 147 single variable functions, 143 two variables functions, 150 Theta, 97, 216 call, 98, 102 finite difference approximation, 191 put, 98 Trapezoidal Rule, 54, 56, 59 Variance, 81, 85 Vega, 97 call, 98, 101 put, 98 vega finite difference approximation, 191 Volatility, 94 Yield curve, 64 Zero rate curve, 64 II I \ I

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Many of these exercises are, in fact, questions that are frequently asked in interviews for quantitative jobs in financial institutions, and some are constructed in a sequential fashion, building upon each other, as is often the case at interviews. Complete solutions to most of the exercises can be found at http://www.fepress.org/ This book can be used as a companion to any more advanced quantitative finance book. It also makes a good reference book for mathematical topics that are frequently assumed to be known in other texts, such as Taylor expansions, Lagrange multipliers, finite difference approximations, and numerical methods for solving nonlinear equations. This book should be useful to a large audience: • Prospective students for financial engineering (or mathematical finance) xiii PREFACE xiv programs will find that the knowledge contained in this book is fundamental for their understanding of more advanced courses on numerical methods for finance and stochastic calculus, while some of the exercises will give them a flavor of what interviewing for jobs upon graduation might be like. • For finance practitioners, while parts of the book will be light reading, the book will also provide new mathematical connections (or present them in a new light) between financial instruments and models used in practice, and will do so in a rigorous and concise manner. • For academics teaching financial mathematics courses, and for their students, this is a rigorous reference book for the mathematical topics required in these courses. • For professionals interested in a career in finance with emphasis on quantitative skills, the book can be used as a stepping stone toward that goal, by building a solid mathematical foundation for further studies, as well as providing a first insight in the world of quantitative finance.

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. • For academics teaching financial mathematics courses, and for their students, this is a rigorous reference book for the mathematical topics required in these courses. • For professionals interested in a career in finance with emphasis on quantitative skills, the book can be used as a stepping stone toward that goal, by building a solid mathematical foundation for further studies, as well as providing a first insight in the world of quantitative finance. The material in this book has been used for a mathematics refresher course for students entering the Financial Engineering Masters Program (MFE) at Baruch College, City University of New York. Studying this material before entering the program provided the students with a solid background and played an important role in making them successful graduates: over 90 percent of the graduates of the Baruch MFE Program are currently employed in the financial industry.

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

Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business process, butterfly effect, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, Chance favours the prepared mind, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative ﬁnance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, short selling, sovereign wealth fund, statistical arbitrage, Steven Pinker, stochastic process, survivorship bias, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

It was part of a new quantitative movement in financial economics, along with Harry Markowitz’s optimal portfolio theory; William Sharpe’s Capital Asset Pricing Model (which we’ll come back to in chapter 8); and Fischer Black, Myron Scholes, and Robert C. Merton’s option pricing formula. These discoveries appeared within a few years of each other, and they illuminated aspects of market behavior that had remained mysterious for centuries. Of all the discoveries in the new quantitative finance movement, however, the Efficient Markets Hypothesis was the crown jewel. These esoteric discoveries not only changed the way economists thought about financial markets, but they also made these markets much more accessible to the general public. The Efficient Markets Hypothesis gave the investor a democratic alternative to following the cult of the investment guru. Instead of having your stocks handpicked by an adviser who was unlikely to beat the market anyway, you could invest in passive, low-cost, broadly diversified mutual funds.

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But a near-perfect illustration of the Adaptive Markets Hypothesis can be found among a small but elite group of financial-industry insiders, shrouded in secrecy and mystery: hedge fund managers. It takes a theory to beat a theory, and the Adaptive Markets Hypothesis is the new contender. But these are still early days for the challenger—the incumbent has had a five-decade head start—and a great deal more research is needed before these ideas become as immediately useful as the existing models of quantitative finance. Also, science itself The Adaptive Markets Hypothesis • 221 is an evolutionary process. Between theory, data, and experiment, the Adaptive Markets Hypothesis will survive, perhaps be replaced with an even more compelling theory in the future, or fall short and be forgotten. But even at this early stage, it’s clear that this hypothesis can gracefully resolve many of the contradictions between the Efficient Markets Hypothesis and its exceptions.

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International Economic Review 10: 1–21. Farmer, J. Doyne. 2002. “Market Force, Ecology, and Evolution.” Industrial and Corporate Change 11: 895–953. Farmer, J. Doyne, and Andrew W. Lo. 1999. “Frontiers of Finance: Evolution and Efficient Markets.” Proceedings of the National Academy of Sciences 96: 9991–9992. Farmer, J. Doyne, and Spyros Skouras. 2013. “An Ecological Perspective on the Future of Computer Trading.” Quantitative Finance 13: 325–346. Fauci, Anthony S., and Gregory K. Folkers. 2012. “Toward an AIDS-Free Generation.” Journal of the American Medical Association 308: 343–344. Fearer, Matthew. 2014. “An Improbable Circle of Life.” Paradigm: Life Sciences at Whitehead Institute For Biomedical Research (Spring): 8–13. Available at http:// wi.mit.edu/fi les/wi/pdf/726/spring2014.pdf. Feinstein, Justin S., Ralph Adolphs, Antonio Damasio, and Daniel Tranel. 2011.

**
Market Risk Analysis, Quantitative Methods in Finance
** by
Carol Alexander

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asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, fixed income, implied volatility, interest rate swap, market friction, market microstructure, p-value, performance metric, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, Thomas Bayes, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-sum game

Wessa, P. (2006) Kernel Density Estimation (V1.0.3) in Free Statistics Software (V1.1.21). Office For Research Development and Education. http://www.wessa.net/rwasp_density.wasp/ (accessed December 2007). White, H. (1980) A heteroscedasticity consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica 48, 817–838. Wilmott, P. (2006). Paul Wilmott on Quantitative Finance, 3 volumes. John Wiley & Sons, Ltd, Chichester. Wilmott, P. (2007). Paul Wilmott Introduces Quantitative Finance. John Wiley & Sons, Ltd, Chichester. Statistical Tables 274 Statistical Tables Statistical Tables 275 276 Statistical Tables Statistical Tables 277 Index Abnormal return, CAPM 253 Absolute return 58 Absolute risk tolerance 231 Absolute value function 6 Active return 92, 256 Active risk 256 Alternative hypothesis 124, 151 American option 1, 215–16 Amex case study 144–6, 153–5 Amex Oil index 162–3, 169–70, 174 Analysis of variance (ANOVA) Amex case study 154 BHP Billiton Ltd case study 164–5 matrix notation 159–60 regression 143–4, 149–50 Analytic solution 185 Anderson–Darling test 128–9 ANOVA (analysis of variance) Amex case study 154 BHP Billiton Ltd case study 164–5 matrix notation 159–60 regression 143–4, 149–50 Appraisal ratio 257 Approximate confidence interval 122 Approximations delta–gamma 2, 34 delta–gamma–vega 34 duration–convexity 2–3, 34 finite difference 206–10, 223 Taylor expansion 31–4, 36 Arbitrage no arbitrage 2, 179–80, 211–13 pricing theory 257 statistical strategy 182–3 Arithmetic Brownian motion 22, 136, 138–9 Arrival rate, Poisson distribution 87–9 Ask price 2 Asset management, global 225 Asset prices binomial theorem 85–7 lognormal distribution 213–14 pricing theory 179–80, 250–55 regression 179–80 stochastic process 137–8 Assets, tradable 1 Asymptotic mean integrated square error 107 Asymptotic properties of OLS estimators 156 Autocorrelation 175–9, 184, 259–62 Autocorrelation adjusted Sharpe ratio 259–62 Autoregression 135 Auxiliary regression 177 Backtesting 183 Backward difference operator 19 Bandwidth, kernel 106–7 Bank 225 Barra model 181 Basic calculus 1–36 Basis splines 200 Bayesian probability 72–3 Bermudan option 1 Bernoulli trial 85–6 Best fit of function to data 201 Best fit line 145 Best linear unbiased estimator (BLUE) 157, 175 280 Index Beta values CAPM 252–3 diversifiable risk 181 OLS estimation 147–8, 156, 160–1, 183–4 regression 156 Bid–ask spread 2 Bid price 2 Bilinear interpolation 193–5 Binomial distribution 85–7, 213 Binomial lattices 186, 210–16, 223 American option 215–16 European option 212–13 lognormal asset price distribution 213–14 no arbitrage 211–12 risk neutral valuation 211–12 Bisection method, iteration 187–8 Bivariate distribution 108–9, 116–17, 148 Bivariate normal distribution 116–17, 148 Bivariate normal mixture distribution 116–17 Black–Scholes–Merton pricing model asset price 137–9 European option 2, 213, 215–16 lognormal distribution 94 numerical method 185 Taylor expansion 2–3 BLUE (best linear unbiased estimator) 157, 175 Bonds 1–2, 37, 191 Bootstrap 218 Brownian motion 136 arithmetic 22, 136, 139 geometric 21–2, 134, 138, 212, 213–14, 218–19 Calculus 1–36 differentiation 10–15 equations and roots 3–9 financial returns 16–26 functions 3–9, 26–31 graphs 3–9 integration 15–16 Taylor expansion 31–4, 36 Calibration 201 Call option 1, 6, 212–13 Capital allocation, bank 225 Capital asset pricing model (CAPM) 179–80, 252–5, 257–8 Capital market line (CML) 250–2 CAPM (capital asset pricing model) 179, 252–5, 257–8 CARA (constant absolute risk aversion) 233–4 Cartesian plane/space 39 Case studies Amex 144–6, 153–5 BHP Billiton Ltd 162–5, 168–70, 174–5, 177–8 credit risk 171–3 EM algorithm 203–6 PCA of European equity index 67–9 time series of asset price 218–20 Cauchy distribution 105 CBOE Gold index 162–3, 168–70, 174 Central limit theorem 120–1 Centre of location of distribution 78–9 Certainty equivalence 227–9 Characteristic equation 51–2 Chi-squared distribution 100–1, 123–4 Cholesky decomposition 37–8, 61–3, 70 Cholesky matrix 62–3, 70, 220–2 Circulant matrix 178 Classical probability 72–3 CML (capital market line) 250–2 Coefficients OLS estimators 155 regression 143–4, 151–2, 155, 168–9 risk aversion 231–4, 237 risk tolerance 233 Cokurtosis, CAPM 255 Complement, probability 73 Complete market 212 Compounding factor, returns 22–3 Concave function 13–14, 35 Conditional confidence interval 169 Conditional distribution 108–9 Conditional mean equation, OLS 148 Conditional probability 73 Conditional value at risk 105 Confidence interval 72, 118–24, 167–70 Conjugate gradient 193 Consistent OLS estimators 156–8 Constant absolute risk aversion (CARA) 233–4 Constant relative risk aversion (CRRA) 232–4 Constant term, regression 143–4 Constrained optimization 29–31 Constraint, minimum variance portfolio 245–6 Continuous compounding, return 22–3 Continuous distribution 114 Continuous function 5–6, 35 Continuous time 134–9 long-short portfolio 21 mean reverting process 136–7 Index notation 16–17 P&L 19 random walks 136–7 stochastic process 134–9 Convergence, iteration 188–9 Convex function 13–14, 35 Copula 109–10 Correlation 111–14 beta value 147–8 simulation 220–2 Correlation matrix 38, 55–61, 70 eigenvalues/vectors 52–4, 59–61 PCA 64–5, 67–8, 70 positive definiteness 58–9 Coskewness, CAPM 255 Counting process 139 Coupon 1 Covariance 80, 110–2 Covariance matrix 37–8, 55–61, 70 eigenvalues/vectors 59–61 OLS estimation 159–60 PCA 64, 66–7, 70 positive definiteness 58–9 Cox–Ross–Rubinstein parameterization 215 Crank–Nicolson finite difference scheme 210 Credit risk case study 171–3 Criterion for convergence, iteration 188 Critical region, hypothesis test 124–5 Critical value 118–20, 122–3, 129 Cross-sectional data 144 CRRA (constant relative risk aversion) 232–4 Cubic spline 197–200 Cumulative distribution function 75 Currency option 195–7 Decision rule, hypothesis test 125 Decomposition of matrix 61–4 Definite integral 15–16 Definite matrix 37, 46–7, 54, 58–9, 70 Degree of freedom, Student t distribution Delta–gamma approximation 2–3, 34 Delta–gamma–vega approximation 34 Delta hedging 208, 211 Density function 75–7 binomial distribution 86 bivariate distribution 108–9 joint 114–15 leptokurtic 82–3 lognormal distribution 93 MLE 130–4 97–8 normal distribution 90–2, 97, 115–17 Poisson distribution 88 stable distribution 105–6 Student t distribution 97–100 Dependent variable 143 Derivatives 1–2 calculation 12–13 first 2, 10–11 partial 27–8, 35 second 2, 11, 13 total 31 Determinant 41–3, 47 Deterministic variable 75 Diagonalizable matrix 43 Diagonal matrix 40, 56 Dicky–Fuller test 136 Differentiable function 5–6, 35 Differential equations partial 2, 208–10 stochastic 134, 136 Differentiation 10–15 concave/convex function 13–14 definition 10–11 monotonic function 13–14 rule 11–12 stationary point 14–15 stochastic differential term 22 Diffusion process, Brownian motion 22 Discontinuity 5 Discrete compounding, return 22–3 Discrete time 134–9 log return 19–20 notation 16–17 P&L 19 percentage return 19–20 random walk model 135 stationary/integrated process 134–6 stochastic process 134–9 Discretization of space 209–10 Discriminant 5 Distribution function 75–7 Diversifiable risk 181 DJIA (Dow Jones Industrial Average) index 137–8 Dot product 39 Dow Jones Industrial Average (DJIA) index 137–8 Dummy variable 175 Duration–convexity approximation 2–3, 34 Durbin–Watson autocorrelation test 176–7 281 282 Index Efficiency, OLS estimators 156–7 Efficient frontier 246–9, 251 Efficient market hypothesis 179 Eigenvalues/vectors 37–8, 48–54, 70 characteristic equation 51–2 correlation matrix 52, 59–60 covariance matrix 59–61 definiteness test 54 definition 50–1 linear portfolio 59–61 matrices/transformations 48–50 properties 52–3 Elliptical distribution 115 EM (expectation–maximization algorithm) 203–6 Empirical distribution 77, 217–18 Enhanced indexation 182–3 Epanechnikov kernel 107 Equality of two mean 126–7 Equality of two variance 126–7 Equations 3–9 CML 252 heat equation 208–9 partial differential 2, 208–10 quadratic 4–5 roots 187 simultaneous equations 44–5 Equity index returns 96–7 Equity price 172 Equity volatility 172–3 Error process 145, 148, 155 ESS (explained sum of squares) 149–50, 159–62 Estimation calibration 201 MLE 72, 130–4, 141, 202–3 OLS 143–4, 146–9, 153–63, 170–1, 176 ETL (expected tail loss) 104–5 European equity indices case study 67–9 European options 1–2 American option 215–16 binomial lattice 212–13 interpolation 195–6 pricing 212–13, 215–16 Euro swap rate (1-year) 172 Excel BHP Billiton Ltd case study 163–4 binomial distribution 213 chi-squared distribution 123–4 critical values 118–20, 122–3 Goal Seek 186, 188–9 histogram 77–8 matrix algebra 40, 43–6, 53–4, 59, 63–4, 68, 70 moments of a sample 82–3 multiple regression 163–4 normal probabilities 90–1 OLS estimation 153–5 percentiles 83–5 Poisson distribution 88 random numbers 89 simulation 217, 219 Solver 186, 190–1, 246 Student t distribution 100, 122–3 Expectation–maximization (EM) algorithm 203–6 Expected tail loss (ETL) 104–5 Expected utility 227–8 Expected value (expectation) 78–9 Explained sum of squares (ESS) 149–50, 159–62 Explanatory variables 143, 157, 170 Explicit function 185 Exponential distribution 87–9 Exponential function 1, 7–9, 34–5, 233–7 Exponential utility function 233–7 Extrapolation 186, 193–200, 223 Extreme value theory 101–3 Factorial function 8 Factorial notation 86 Factor model software 181 F distribution 100–1, 127 Feasible set 246 Finance calculus 1–36 essential algebra 37–70 numerical methods 185–223 portfolio mathematics 225–67 Financial market integration 180–1 Finite difference approximation 186, 206–10, 223 first/second order 206–7 the Greeks 207–8 partial differential equations 208–10 First difference operator, discrete time 17 First order autocorrelation 178 Forecasting 182, 254 Forward difference operator, returns 19, 22 Index Fréchet distribution 103 F test 127 FTSE 100 index 204–5, 242–4 Fully-funded portfolio 18 Functions 3–9, 26–31 absolute value 6 concave 13–14, 35 continuous 5–6, 35 convex 13–14, 35 differentiable 5–6, 35 distribution function 75–7, 114–15 explicit/implicit 185 exponential 1, 7–9, 34–5, 234–7 factorial 8 gamma 97–8 indicator 6 inverse 6–7, 35 Lagrangian 29–30 likelihood 72, 130–4 linear 4–5 logarithmic 1, 9, 34–5 monotonic 13–14, 35 non-linear 1–2 objective 29, 188 quadratic 4–5, 233–4 several variables 26–31 utility 232–4 Fundamental theorem of arbitrage 212 Future 1, 181–2, 194 Gamma function, Student t distribution 97–8 Gaussian copula 109–10 Gaussian kernel 107 Gauss–Markov theorem 157, 175, 184 Generalized extreme value (GEV) distribution 101–3 Generalized least squares (GLS) 178–9 Generalized Pareto distribution 101, 103–5 Generalized Sharpe ratio 262–3 General linear model, regression 161–2 Geometric Brownian motion 21–2 lognormal asset price distribution 213–14 SDE 134 stochastic process 141 time series of asset prices 218–20 GEV (generalized extreme value) distribution 101–3 Global asset management 225 Global minimum variance portfolio 244, 246–7 283 GLS (generalized least squares) 178–9 Goal Seek, Excel 186, 188–9 Gold index, CBOE 162–3, 168–70, 174 Goodness of fit 128, 149–50, 163–5, 167 Gradient vector 28 Graphs 3–9 The Greeks 207–8 Gumbel distribution 103 Heat equation 208 Hedging 2, 181–2 Hermite polynomials 200 Hessian matrix 28–30, 132, 192–3 Heteroscedasticity 175–9, 184 Higher moment CAPM model 255 Histogram 76–8 Historical data, VaR 106 Homoscedasticity 135 h-period log return 23–4 Hyperbola 5 Hypothesis tests 72, 124–5 CAPM 254–5 regression 151–2, 163–6 Student t distribution 100 Identity matrix 40–1 i.i.d.

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Readers will be introduced to financial concepts through mathematical applications, so they will be able to identify which parts of mathematics are relevant to solving problems in finance, as well as learning the basics of financial analysis (in the mathematical sense) and how to apply their skills to particular problems in financial risk management and asset management. AIMS AND SCOPE This book is designed as a text for introductory university and professional courses in quantitative finance. The level should be accessible to anyone with a moderate understanding of mathematics at the high school level, and no prior knowledge of finance is necessary. For ease of exposition the emphasis is on understanding ideas rather than on mathematical rigour, although the latter has not been sacrificed as it is in some other introductory level texts. Illustrative examples are provided immediately after the introduction of each new concept in order to make the exposition accessible to a wide audience.

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As a result two different types of notation are used for the same object and the same model is expressed in two different ways. One of the features that makes this book so different from many others is that I focus on both continuous and discrete time finance, and explain how the two areas meet. Although the four volumes of Market Risk Analysis are very much interlinked, each book is self-contained. This book could easily be adopted as a stand-alone course text in quantitative finance or quantitative risk management, leaving more advanced students to follow up cross references to later volumes only if they wish. The other volumes in Market Risk Analysis are: Volume II: Practical Financial Econometrics Volume III: Pricing, Hedging and Trading Financial Instruments Volume IV: Value at Risk Models. Preface xxv OUTLINE OF VOLUME I This volume contains sufficient material for a two-semester course that focuses on basic mathematics for finance or financial risk management.

**
Python for Finance
** by
Yuxing Yan

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asset-backed security, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, distributed generation, diversified portfolio, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative ﬁnance, Sharpe ratio, time value of money, value at risk, volatility smile, zero-sum game

If your book idea is still at an early stage and you would like to discuss it first before writing a formal book proposal, contact us; one of our commissioning editors will get in touch with you. We're not just looking for published authors; if you have strong technical skills but no writing experience, our experienced editors can help you develop a writing career, or simply get some additional reward for your expertise. Introduction to R for Quantitative Finance ISBN: 978-1-78328-093-3 Paperback: 164 pages Solve a diverse range of problems with R, one of the most powerful tools for quantitative finance 1. Use time series analysis to model and forecast house prices. 2. Estimate the term structure of interest rates using prices of government bonds. 3. Detect systemically important financial institutions by employing financial network analysis . Python High Performance Programming ISBN: 978-1-78328-845-8 Paperback: 108 pages Boost the performance of your Python programs using advanced techniques 1.

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Python for Finance Build real-life Python applications for quantitative finance and financial engineering Yuxing Yan BIRMINGHAM - MUMBAI Python for Finance Copyright © 2014 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

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One feature of this book is that most chapters after Chapter 3, Using Python as a Financial Calculator, are loosely connected. Because of this, after learning the first three chapters in addition to Chapter 5, Introduction to Modules, readers could jump to the chapters they are interested in. On the other hand, the book is ideal to be used as a textbook for Financial Modeling using Python or simply Python for finance courses to master degree students in the areas of quantitative finance, computational finance, or financial engineering. The amount of content of the book and expected effort needed is suitable for one semester. The students could be senior undergraduate students with a reduced depth. To teach undergraduate students, the last chapter should be dropped. Reader feedback Feedback from our readers is always welcome. Let us know what you think about this book—what you liked or may have disliked.

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Why Stock Markets Crash: Critical Events in Complex Financial Systems
** by
Didier Sornette

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Asian financial crisis, asset allocation, Berlin Wall, Bretton Woods, Brownian motion, capital asset pricing model, capital controls, continuous double auction, currency peg, Deng Xiaoping, discrete time, diversified portfolio, Elliott wave, Erdős number, experimental economics, financial innovation, floating exchange rates, frictionless, frictionless market, full employment, global village, implied volatility, index fund, information asymmetry, intangible asset, invisible hand, John von Neumann, joint-stock company, law of one price, Louis Bachelier, mandelbrot fractal, margin call, market bubble, market clearing, market design, market fundamentalism, mental accounting, moral hazard, Network effects, new economy, oil shock, open economy, pattern recognition, Paul Erdős, Paul Samuelson, quantitative trading / quantitative ﬁnance, random walk, risk/return, Ronald Reagan, Schrödinger's Cat, selection bias, short selling, Silicon Valley, South Sea Bubble, statistical model, stochastic process, Tacoma Narrows Bridge, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, Tobin tax, total factor productivity, transaction costs, tulip mania, VA Linux, Y2K, yield curve

Algorithmic Information Theory (Cambridge University Press, Cambridge, New York). 74. Chaline, J., Nottale, L., and Grou, P. (1999). L’arbre de la vie a-t-il une structure fractale? Comptes Rendus l’Académie des Sciences, Paris 328, 717–726. 75. Challet, D. Minority Game’s web page: http://www.unifr.ch/econophysics/minority/ minority.html. 76. Challet, D., Chessa, A., Marsili, M., and Zhang, Y.-C. (2001). From minority games to real markets, Quantitative Finance 1 (1), 168–176. 77. Challet, D. and Zhang, Y.-C. (1997). Emergence of cooperation and organization in an evolutionary game, Physica A 246, 407–418. r efe rences 401 78. Challet, D. and Zhang, Y.-C. (1998). On the minority game: Analytical and numerical studies, Physica A 256, 514–532. 79. Chan, N. T., LeBaron, B., Lo, A. W., and Poggio, T. (1999). Agent-Based Models of Financial Markets: A Comparison with Experimental Markets, Working paper, MIT, Cambridge, MA; preprint at http://cyber-exchange.mit.edu/. 80.

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The price dynamics of common trading strategies, to appear in the Journal of Economic Behavior and Organization; e-print at http://xxx.lanl.gov/abs/cond-mat/0012419. 125. Feather, N. T. (1968). Change in conﬁdence following success or failure as a predictor of subsequent performance, Journal of Personality and Social Psychology 9, 38–46. 126. Feder, J. (1988). Fractals (Plenum Press, New York). 127. Feigenbaum, J. A. (2001). A statistical analysis of log-periodic precursors to ﬁnancial crashes, Quantitative Finance 1, 346–360. 128. Feigenbaum, J. A. and Freund, P. G. O. (1996). Discrete scale invariance in stock markets before crashes, International Journal of Modern Physics B 10, 3737–3745. 129. Feigenbaum, J. A. and Freund, P. G. O. (1998). Discrete scale invariance and the “second Black Monday,” Modern Physics Letters B 12, 57–60. 130. Feldman, R. A. (1982). Dollar appreciation, foreign trade, and the U.S. economy, Federal Reserve Bank of New York Quarterly Review 7, 1–9. 131.

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A statistical derivation of the signiﬁcant-digit law, Statistical Science 10, 354–363. 197. Hoffman, E. (1991). Bibliography of Experimental Economics, Department of Economics, University of Arizona, Tucson, 1991. 198. Holland, J. H. (1992). Complex adaptive systems, Daedalus 121, 17–30. 199. Holmes, P. A. (1985). How fast will the dollar drop? Nation’s Business 73, 16. 200. Hommes, C. H. (2001). Financial markets as nonlinear adaptive evolutionary systems, Quantitative Finance 1, 149–167. 201. Hsieh, D. A. (1989). Testing for nonlinear dependence in daily foreign exchange rates, Journal of Business 62, 339–368. 202. Hsieh, D. A. (1995). Nonlinear dynamics in ﬁnancial markets: evidence and implications, Financial Analysts Journal July–August, 55–62. 203. Huang, Y., Johansen, A., Lee, M. W., Saleur, H., and Sornette, D. (2000). Artifactual log-periodicity in ﬁnite-size data: Relevance for earthquake aftershocks, Journal of Geophysics Research 105, 25451–25471. 204.

**
Extreme Money: Masters of the Universe and the Cult of Risk
** by
Satyajit Das

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affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, financial independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, labour market flexibility, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, negative equity, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, Plutocrats, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative ﬁnance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

War Versus Money Scientists traded in real union cards to trade on Wall Street as available academic and defence jobs decreased. They flocked to make money not war. Each year 150,000 people in the United States alone sit examinations to earn the letters CFA—certified financial analyst. The CFA and financial engineering program such as CQF (certificate in quantitative finance) were an entrance ticket to lucrative careers on Wall Street. Sylvain Raines, an experienced quant, joked that quantitative finance was an oxymoron as “finance is quantitative by definition...this is like saying aerial flight or wet swimming.”11 Newly graduated financial experts applied simple “phenomenological toys” to markets. Most financial models are wrong, only the degree of error is in question. Differential equations, positive definite matrices or the desirable statistical properties of an estimator rarely determine the price of traded financial instruments.

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My understanding that talent was innate and skill was acquired is obviously incorrect. Whereas once a basic business degree or (for those with higher aspirations) an MBA (Master of Business Administration) was sufficient, these days the qualification of choice is the M.Fin. (Master of Finance), M.App.Fin. (Master of Applied Finance), M.Sc. (Fin.) (Master of Science in Finance), CFA (Certified Financial Analyst), CQF (Certificate in Quantitative Finance), and so on. Perhaps there should be a new qualification—MMM (Master of Making Money). The alphabet soup feeds an industry, providing the training that is necessary to keep up to date or risk losing the qualification. Multiple streams cater for the varied audience—fascists, anarchists, neo-cons, Fabian socialists, Marxist-Leninists, Friedmanites, Keynesians, Roundheads, Cavaliers, and militant vegans.

…

In late December 2007, his positions showed profits of $2 billion, before the markets turned and the gains became losses. Kerviel was not greedy, hoping only for a modest €300,000 bonus. But he wanted recognition as an exceptional trader: “This will show the power of Kerviel.”3 Kerviel admitted: “You lose a sense of the sums involved in this type of work. You get desensitized.”4 Soldier Monks ShockGen damaged French banking’s image of cutting-edge quantitative finance. In May 2007, Christian Noyer, the governor of the Banque de France, praised French banks’ derivative skills: “French expertise in this field is founded on a solid teaching in math and finance, the key to excellency in financial fields, and on an important hive of university-level talents.”5 At SG Antoine Paille built the derivative business in the 1980s, drawing on the skills of highly trained engineers and mathematicians from the grande écoles.

**
The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton
** by
Colin Read

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Albert Einstein, Bayesian statistics, Black-Scholes formula, Bretton Woods, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, discovery of penicillin, discrete time, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, floating exchange rates, full employment, Henri Poincaré, implied volatility, index fund, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market clearing, martingale, means of production, moral hazard, Myron Scholes, naked short selling, Paul Samuelson, price stability, principal–agent problem, quantitative trading / quantitative ﬁnance, RAND corporation, random walk, risk tolerance, risk/return, Ronald Reagan, shareholder value, Sharpe ratio, short selling, stochastic process, The Chicago School, the scientific method, too big to fail, transaction costs, tulip mania, Works Progress Administration, yield curve

By 1964, the necessary algorithms and programs were in place, William Sharpe was finally able to publish his paper on capital asset pricing, and IBM began selling a revolutionary new computer, the IBM 360. This computer filled a room, cost hundreds of thousands of dollars, required technicians to operate it, and performed up to 34,000 instructions per second.1 To put this computing power in perspective, a modern smartphone can calculate tens of thousands of times faster. Nonetheless, with the theoretical, software, and hardware tools suddenly available, quantitative finance analysts could finally get to work in pricing securities and determining the extent that securities price movements either fit or depart from our intuition. First, Modern Portfolio Theory and, soon, the CAPM led the way. The CAPM has defined finance theory and revolutionized practice ever since. The big idea In 1936 and 1937 two scholars had presented two wildly divergent pictures of the pricing of financial securities.

…

No other discovery or innovation has directly generated the sort of profits and economic activity that these innovations in finance created. Nor did any of these other innovations give rise to entirely new academic disciplines. The ability to transform markets, create a plethora of new, timely, and useful research, and the creation of almost unfathomable wealth are the legacies of this handful of scholars who forged the new American school of finance. In the creation of quantitative finance, some of these great minds were cut by a double-edged sword. Not only can these new theories create great wealth, but the confidence they engender in an uncertain financial world has also helped to create great hardship, financial destruction, and even a global financial meltdown. It is interesting to observe that even these great minds were not even personally immune to financial hardship when markets go terribly wrong in ways that cannot be anticipated by equations riddled with Greek letters and based on backward-looking measures of risk.

**
Makers and Takers: The Rise of Finance and the Fall of American Business
** by
Rana Foroohar

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3D printing, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, additive manufacturing, Airbnb, algorithmic trading, Alvin Roth, Asian financial crisis, asset allocation, bank run, Basel III, bonus culture, Bretton Woods, British Empire, call centre, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, centralized clearinghouse, clean water, collateralized debt obligation, commoditize, computerized trading, corporate governance, corporate raider, corporate social responsibility, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, crowdsourcing, David Graeber, deskilling, Detroit bankruptcy, diversification, Double Irish / Dutch Sandwich, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial deregulation, financial intermediation, Frederick Winslow Taylor, George Akerlof, gig economy, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, High speed trading, Home mortgage interest deduction, housing crisis, Howard Rheingold, Hyman Minsky, income inequality, index fund, information asymmetry, interest rate derivative, interest rate swap, Internet of things, invisible hand, John Markoff, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, knowledge economy, labor-force participation, labour mobility, London Whale, Long Term Capital Management, manufacturing employment, market design, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, new economy, non-tariff barriers, offshore financial centre, oil shock, passive investing, Paul Samuelson, pensions crisis, Ponzi scheme, principal–agent problem, quantitative easing, quantitative trading / quantitative ﬁnance, race to the bottom, Ralph Nader, Rana Plaza, RAND corporation, random walk, rent control, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Snapchat, sovereign wealth fund, Steve Jobs, technology bubble, The Chicago School, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tim Cook: Apple, Tobin tax, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, Vanguard fund, zero-sum game

While American business schools largely missed big-picture shifts like the Japanese-led quality revolution of the 1970s and the PC boom of the late 1970s and ’80s (not to mention the rise of massive networking and mobile technologies a decade later), they have led the way in mathematical finance, which became the basis of the shift in banking from lending to trading. These business schools also spurred the growth of the “portfolio society” in which everything—from stocks and bonds to hospital beds and even human lifespans—has a market price. The key early player in this area was Harry Markowitz, another Chicago student who had worked for RAND in the 1950s and did his PhD under Friedman. Markowitz’s quantitative finance methodology won him the Nobel Prize and became the basis of the first computerized arbitrage-trading program, which would eventually take over the markets. Today 70–80 percent of all trading is done by computers, much of it using flash programs designed to trade on fractional price changes over split-second time intervals, reducing the average holding period of a stock from about eight years in the 1960s to just four months by 2012.55 Emanuel Derman, a quantitative mathematician and physicist who pioneered some of those trading models at Goldman and now teaches financial engineering at Columbia, believes that the focus on mathematical economics in both finance and business education has gone way too far.

…

Today 70–80 percent of all trading is done by computers, much of it using flash programs designed to trade on fractional price changes over split-second time intervals, reducing the average holding period of a stock from about eight years in the 1960s to just four months by 2012.55 Emanuel Derman, a quantitative mathematician and physicist who pioneered some of those trading models at Goldman and now teaches financial engineering at Columbia, believes that the focus on mathematical economics in both finance and business education has gone way too far. Indeed, in 2012, he published a mea culpa for his own work in the twentieth-anniversary issue of the Journal of Derivatives. “Models of all kinds, ethical and quantitative too, have been behaving very badly,” he wrote. The problem, he believes, is that practitioners of quantitative finance have come to believe that it can in fact have the predictive power of physics, when in reality financial modeling will always be fallible, because it’s a discipline based on human behavior. “To confuse a model with the world of humans is a form of idolatry—and dangerous.”56 Yet that danger is only increasing. At places like Harvard, the percentage of MBA students going into finance as a whole has dropped slightly from its peak (31 percent today versus 39 percent before the crisis), but the share who are bringing skills like mathematical finance to areas such as private equity, venture capital, and high tech is increasing.57 Indeed, at many of the country’s top MBA programs, students report that recruitment fairs are still dominated by financiers and financial institutions, whether they be traditional banks and consulting firms or small boutique companies.

…

In Lo’s world, market participants aren’t coldly rational creatures but squirmy, evolving species interacting with one another in a primordial sludge of money. By tracking the data trails left by this Darwinian process, we might be able to get a better picture of how markets really work. Lo’s teaching involves not just modeling the abstract, but analyzing the real—what people, companies, regulators, and market participants really do on Main Street. And although he uses many of the tools of quantitative finance in his work, one assumption he never makes is that markets are rational. “Practice without theory is not very effective. And theory without practice can be dangerous,” says Lo. “Economics has had physics envy, but ultimately, economics is all about human behavior.” Some behavior we can model. But many other times, the directions of the markets and the participants are totally unexpected.

**
A Man for All Markets
** by
Edward O. Thorp

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3Com Palm IPO, Albert Einstein, asset allocation, beat the dealer, Bernie Madoff, Black Swan, Black-Scholes formula, Brownian motion, buy low sell high, carried interest, Chuck Templeton: OpenTable, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, compound rate of return, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial innovation, George Santayana, German hyperinflation, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Meriwether, John Nash: game theory, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, margin call, Mason jar, merger arbitrage, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, price anchoring, publish or perish, quantitative trading / quantitative ﬁnance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, Richard Feynman, risk-adjusted returns, Robert Shiller, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, statistical arbitrage, stem cell, survivorship bias, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

Chapter 15 * * * RISE… On November 1, 1979, ten years after we started Princeton Newport Partners, the annualized return for the S&P 500, including dividends, was 4.6 percent and for small-company stocks 8.5 percent, both with far more volatility than Princeton Newport. We were up 409 percent for the decade, annualizing at 17.7 percent before fees and 14.1 percent after fees. Our initial $1.4 million had grown to $28.6 million. We ended 1979 with a grand dream for the 1980s: to expand our expertise into new investment areas. For me this meant more interesting problems to solve in quantitative finance. For the partnership it could lead to an increase in the amount of capital we could invest at high rates of return. I called our first effort the Indicators Project. The object was to study the financial characteristics of companies, or indicators, to see if they could be used to forecast stock returns. The prototype was Value Line, an investment service that launched a program in 1965 using information such as surprise earnings announcements, price-to-earnings ratios, and momentum to rank stocks into groups from I (best) to V (worst).

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I have a gold-colored plaque, a so-called deal toy, on my desk commemorating the December 1, 1983, block as then being the largest dollar amount for a single trade in the history of the New York Stock Exchange. In two and a half months, PNP netted $1.6 million from the AT&T trade after all costs. Meanwhile, an army of PhDs, following our path, greatly expanded the theory of derivatives and implemented the revolution in quantitative finance on Wall Street. They helped direct investing at hedge funds, investment banks, and other institutions. Driven in part by the sell side—the sales force that finds and sells new products—these quants invented new derivative securities that the salespeople then pushed. These products undermined the world financial system in a series of increasingly grave crises. The first of these surprised almost everyone.

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Forbes, page 20, says the formula uses the exponent 0.7, which equals 1/B and, therefore, a value for B of 1.43. Their formula inverts mine and expresses W as a function of N. For an extended discussion of formulas for estimating wealth and income, including evidence for the Pareto equation, see Inhaber and Carroll (1992) and the many further references therein as well as Scafetta, Picozzi and West, “An out-of-equilibrium model of the distribution of wealth,” Quantitative Finance, Vol. 4 (2004) pp. 353–64. billion dollars or more The Orange County Business Journal listed 36, with Lakers basketball star Kobe Bryant in 36th place at $250 million. Since I know of people they missed, the figure of 49 may be closer to the truth. $81 billion The Gates household had as much wealth as 150 thousand average US households. In other words, one one-thousandth of all the private wealth in the United States.

**
R Cookbook
** by
Paul Teetor

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Debian, en.wikipedia.org, p-value, quantitative trading / quantitative ﬁnance, statistical model

In summary, it seems there are three groups of stocks here: SUN and VLO APC and BP Everything else Factor analysis is an art and a science. I suggest that you read a good book on multivariate analysis before employing it. See Also See Recipe 13.4 for more about principal components analysis. Chapter 14. Time Series Analysis Introduction Time series analysis has become a hot topic with the rise of quantitative finance and automated trading of securities. Many of the facilities described in this chapter were invented by practitioners and researchers in finance, securities trading, and portfolio management. Before starting any time series analysis in R, a key decision is your choice of data representation (object class). This is especially critical in an object-oriented language such as R because the choice affects more than how the data is stored; it also dictates which functions (methods) will be available for loading, processing, analyzing, printing, and plotting your data.

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See Also See CRAN for documentation on zoo and xts; this documentation includes reference manuals, vignettes, and quick reference cards. If the packages are already installed on your computer, view their documentation using the vignette function: > vignette("zoo") > vignette("xts") The timeSeries package is another good implementation of a time series object. It is part of the Rmetrics project for quantitative finance. 14.2. Plotting Time Series Data Problem You want to plot one or more time series. Solution Use plot(x), which works for zoo objects and xts objects containing either single or multiple time series. For a simple vector v of time series observations, you can use either plot(v,type="l") or plot.ts(v). Discussion The generic plot function has a version for zoo objects and xts objects.

**
The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds
** by
Maneet Ahuja,
Myron Scholes,
Mohamed El-Erian

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activist fund / activist shareholder / activist investor, Asian financial crisis, asset allocation, asset-backed security, backtesting, Bernie Madoff, Bretton Woods, business process, call centre, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, fixed income, high net worth, interest rate derivative, Isaac Newton, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, merger arbitrage, Myron Scholes, NetJets, oil shock, pattern recognition, Ponzi scheme, quantitative easing, quantitative trading / quantitative ﬁnance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, systematic trading, zero-sum game

“Having a firmer link with an academic institution would help us in terms of hiring people and getting to know the latest advances in academics and research, and might even give us some of new ideas that we could actually capitalize on,” says Wong. After some exploration, AHL focused on Oxford, mostly because the university had a clear vision for the collaboration. The result is the Oxford Man Institute of Quantitative Finance. “They offered us an entire department within the school,” says Wong. “We have an office there with a partner who is physically on site, and about another dozen of our people are there interacting with about 50 academics on a daily basis. And that’s a great match: academics love to solve problems, and we love to ask people to solve our problems.” The Man Group has contributed 13 million pounds to the venture, and Wong believes the investment has been definitely worth it.

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Lebow, Bennett Leucadia National Corporation Levy, Leon Loeb, Daniel. See also Third Point LTV Lueck, Martin Man Group/AHL Marconi Corporation Marks, Leonard Marriott Corporation MBIA McCormick, David McDonald’s Mint Investment Management Co. Morgan Stanley Investment Management Murray, Eileen Nash, Jack Neumann, Michael Odyssey Partners Oxford Man Institute of Quantitative Finance Paulson, John address to Committee on Oversight and Government Reform Advantage funds Bank of America, investment in Bear Stearns Citigroup, investment in gold share class, creation of Gruss Partners merger of Dow Chemical and Rohm & Haas Odyssey Partners Paulson Credit Fund Paulson Partners Pellegrini, Paolo Pershing Square Capital Management.

**
Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
** by
William Poundstone

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Albert Einstein, anti-communist, asset allocation, beat the dealer, Benoit Mandelbrot, Black-Scholes formula, Brownian motion, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, Edward Thorp, en.wikipedia.org, Eugene Fama: efficient market hypothesis, high net worth, index fund, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, John Meriwether, John von Neumann, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Myron Scholes, New Journalism, Norbert Wiener, offshore financial centre, Paul Samuelson, publish or perish, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Rubik’s Cube, short selling, speech recognition, statistical arbitrage, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, value at risk, zero-coupon bond, zero-sum game

In October 1994, LTCM sent its investors a document comparing projected returns to risks. One reported factoid: In order to make a 25 percent annual return, the fund would have to assume a 1 percent chance of losing 20 percent or more of the fund’s value in a year. A 20-percent-or-more loss was the worst case considered. The chapter on Value at Risk in the popular finance textbook Paul Wilmott Introduces Quantitative Finance begins with a cartoon of the author shrugging. “I’ve got a bad feeling about this…” he says. Wilmott isn’t alone. There are at least two problems with VaR. One is that it plays into the mystique of numbers. The consumer of VaR reports is led to believe that the numbers are reliable because smart people have gone to a lot of trouble to work them out. The numbers are only as good as the assumptions underlying them.

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Journal of Portfolio Management, Spring 2003. ———(2004). “Risk Management: Survival of the Fittest.” Journal of Asset Management, June 2004, 13–24. Wiles, Russ, and Cameron P. Hum (1986). “Calculated Risk Taker.” Plaza Communications. Williams, J. B. (1936). “Speculation and the Carryover.” Quarterly Journal of Economics, May 1936, 436–55. Wilmott, Paul (2001). Paul Wilmott Introduces Quantitative Finance. New York: Wiley. Wolfe, Tom (1961). “Round 2: Math Professor vs. Las Vegas Casinos.” Detroit News, datelined Dec. 14, 1961. Ziemba, William T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. Charlottesville, Va.: Research Foundation of AIMR. ———, and Donald B. Hausch (1984). Beat the Racetrack. San Diego: Harcourt Brace Jovanovich. Revised as Dr.

**
Antifragile: Things That Gain From Disorder
** by
Nassim Nicholas Taleb

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Air France Flight 447, Andrei Shleifer, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, Chuck Templeton: OpenTable, commoditize, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, financial independence, Flash crash, Gary Taubes, George Santayana, Gini coefficient, Henri Poincaré, high net worth, hygiene hypothesis, Ignaz Semmelweis: hand washing, informal economy, invention of the wheel, invisible hand, Isaac Newton, James Hargreaves, Jane Jacobs, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, Marc Andreessen, meta analysis, meta-analysis, microbiome, money market fund, moral hazard, mouse model, Myron Scholes, Norbert Wiener, pattern recognition, Paul Samuelson, placebo effect, Ponzi scheme, principal–agent problem, purchasing power parity, quantitative trading / quantitative ﬁnance, Ralph Nader, random walk, Ray Kurzweil, rent control, Republic of Letters, Ronald Reagan, Rory Sutherland, selection bias, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, The Great Moderation, the new new thing, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Malthus, too big to fail, transaction costs, urban planning, Vilfredo Pareto, Yogi Berra, Zipf's Law

So it is easy to see that history is truly written by losers with time on their hands and a protected academic position. The greatest irony is that we watched firsthand how narratives of thought are made, as we were lucky enough to face another episode of blatant intellectual expropriation. We received an invitation to publish our side of the story—being option practitioners—in the honorable Wiley Encyclopedia of Quantitative Finance. So we wrote a version of the previous paper mixed with our own experiences. Shock: we caught the editor of the historical section, one Barnard College professor, red-handed trying to modify our account. A historian of economic thought, he proceeded to rewrite our story to play down, if not reverse, its message and change the arrow of the formation of knowledge. This was scientific history in the making.

…

If you think that the statistician really understands “statistical significance” in the complicated texture of real life (the “large world,” as opposed to the “small world” of textbooks), some surprises. Kahneman and Tversky showed that statisticians themselves made practical mistakes in real life in violation of their teachings, forgetting that they were statisticians (thinking, I remind the reader, requires effort). My colleague Daniel Goldstein and I did some research on “quants,” professionals of quantitative finance, and realized that the overwhelming majority did not understand the practical effect of elementary notions such as “variance” or “standard deviation,” concepts they used in about every one of their equations. A recent powerful study by Emre Soyer and Robin Hogarth showed that many professionals and experts in the field of econometrics supplying pompous numbers such as “regression” and “correlation” made egregious mistakes translating into practice the numbers they were producing themselves—they get the equation right but make severe translation mistakes when expressing it into reality.

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Franks, and J. M. Pasteels, 1989, “The Blind Leading the Blind: Modelling Chemically Mediated Army Ant Raid Patterns.” Journal of Insect Behavior 2: 719–725. Deneubourg, J. L., J. M. Pasteels, and J. C. Verhaeghe, 1983, “Probabilistic Behavior in Ants: A Strategy of Errors?” Journal of Theoretical Biology 105: 259–271. Derman, E., and N. N. Taleb, 2005, “The Illusions of Dynamic Replication.” Quantitative Finance 5: 4. Dhabhar, F. S., 2009, “Enhancing Versus Suppressive Effects of Stress on Immune Function: Implications for Immunoprotection and Immunopathology.” Neuroimmunomodulation 16(5): 300–317. Dhabhar, F. S., A. N. Saul, C. Daugherty, T. H. Holmes, D. M. Bouley, T. M. Oberyszyn, 2010, “Short-term Stress Enhances Cellular Immunity and Increases Early Resistance to Squamous Cell carcinoma.” Brain, Behavior and Immunity 24(1): 127–137.

**
The Concepts and Practice of Mathematical Finance
** by
Mark S. Joshi

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Black-Scholes formula, Brownian motion, correlation coefficient, Credit Default Swap, delta neutral, discrete time, Emanuel Derman, fixed income, implied volatility, incomplete markets, interest rate derivative, interest rate swap, London Interbank Offered Rate, martingale, millennium bug, quantitative trading / quantitative ﬁnance, short selling, stochastic process, stochastic volatility, the market place, time value of money, transaction costs, value at risk, volatility smile, yield curve, zero-coupon bond

Joshi, Achieving smooth convergence for the prices of European options in binomial trees, Quantitative Finance 3(6), 2003, 458-69. [88] M. Joshi, A simple derivation of and improvements to Jamshidian's and Rogers' upper bound methods for Bermudan options, Applied Mathematical Finance 14(3), 2007,197-205. [89] M. Joshi, Achieving higher order convergence for the prices of European options in binomial trees, preprint, 2007. [90] M. Joshi, More Mathematical Finance, forthcoming. References 530 [91] M. Joshi, The convergence of binomial trees for pricing the American put, preprint, 2007. [92] M. Joshi, R. Rebonato, A stochastic-volatility displaced-diffusion extension of the LIBOR market model, QUARC Royal Bank of Scotland working paper, 2001. [93] M. Joshi, J. Theis, Bounding Bermudan swaptions in a swap-rate market model, Quantitative Finance 2, 2002, 370-7. [94] I.

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Kincaid, Numerical Analysis: Mathematics of Scientific Computing, Brooks/Cole, 2001. [37] L. Clewlow, C. Strickland, Implementing Derivatives Models, Wiley, 1998. [38] L. Clewlow, C. Strickland, Exotic Options: the State of the Art, Thompson International Press, 1997. [39] J.H. Cochrane, Asset Pricing, Princeton University Press, 2001. [40] R. Cont, Jose da Fonseca, Dynamics of implied volatility surfaces, Quantitative Finance 2, 2002, 45-60. [41] R. Cont, P. Tankov, Financial Modelling with Jump Processes, Chapman & Hall, 2003. [42] J.C. Cox, S. Ross, M. Rubinstein, Option Pricing: a simplified approach, Journal of Financial Economics 7, 1979, 229-63. [43] M. Curran, Beyond average intelligence, Risk 5, 1992, 60. References 528 [44] M. Curran, Valuing Asian and portfolio options by conditioning on the geometric mean price, Management Science 40, 1994, 1705-11. [45] F.

**
No One Would Listen: A True Financial Thriller
** by
Harry Markopolos

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backtesting, barriers to entry, Bernie Madoff, call centre, centralized clearinghouse, correlation coefficient, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, family office, financial thriller, fixed income, forensic accounting, high net worth, index card, Long Term Capital Management, Louis Bachelier, offshore financial centre, Ponzi scheme, price mechanism, quantitative trading / quantitative ﬁnance, regulatory arbitrage, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, statistical arbitrage, too big to fail, transaction costs, your tax dollars at work

Under no circumstances is this report or its contents to be shared with any other regulatory body without my express permission. This report has been written solely for the SEC’s internal use. As far as I know, none of the hedge fund, fund of funds (FOF’s) mentioned in my report are engaged in a conspiracy to commit fraud. I believe they are naive men and women with a notable lack of derivatives expertise and possessing little or no quantitative finance ability. There are 2 possible scenarios that involve fraud by Madoff Securities: 1. Scenario # 1 (Unlikely): I am submitting this case under Section 21A(e) of the 1934 Act in the event that the broker-dealer and ECN depicted is actually providing the stated returns to investors but is earning those returns by front-running customer order flow. Front-running qualifies as insider-trading since it relies upon material, non-public information that is acted upon for the benefit of one party to the detriment of another party.

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He earned his Chartered Financial Analyst designation in 1996 and his Certified Fraud Examiner designation in 2008. From 2002 to 2003 he served as president and CEO of the 4,000-member Boston Security Analysts Society. He has also held board seats on the Boston chapters of both the Global Association of Risk Professionals and the Quantitative Work Alliance for Applied Finance, Education and Wisdom (QWAFAFEW), a quantitative finance lecture group. He was assistant controller, assistant manager, store manager, and district manager for his family’s chain of 12 Arthur Treacher’s Fish & Chips restaurants before joining Makefield Securities in 1987. In 1988 he joined Darien Capital Management in Greenwich, Connecticut, as an assistant portfolio manager, leaving to become an equity derivatives portfolio manager at Rampart Investment Management Company in Boston, Massachusetts.

**
How Markets Fail: The Logic of Economic Calamities
** by
John Cassidy

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Albert Einstein, Andrei Shleifer, anti-communist, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black-Scholes formula, Bretton Woods, British Empire, capital asset pricing model, centralized clearinghouse, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, corporate raider, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Daniel Kahneman / Amos Tversky, debt deflation, diversification, Elliott wave, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, George Akerlof, global supply chain, Gunnar Myrdal, Haight Ashbury, hiring and firing, Hyman Minsky, income per capita, incomplete markets, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Arrow, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Ponzi scheme, price discrimination, price stability, principal–agent problem, profit maximization, quantitative trading / quantitative ﬁnance, race to the bottom, Ralph Nader, RAND corporation, random walk, Renaissance Technologies, rent control, Richard Thaler, risk tolerance, risk-adjusted returns, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game

Investing in index funds, which keep their fees at minimal levels, is much more sensible. By 2000, tens of millions of Americans had taken Malkiel’s advice and placed much of their retirement money in these types of savings vehicles. (For many years, Malkiel served as a director of the Vanguard Group, which pioneered index funds. Fama joined another firm that manages index funds, Dimensional Fund Advisors.) The rise of efficient market theory also signaled the beginning of quantitative finance. In addition to the random walk model of stock prices, the period between 1950 and 1970 saw the development of the mean-variance approach to portfolio diversification, which Harry Markowitz, another Chicago economist, pioneered; the capital asset pricing model, which a number of different scholars developed independently of one another; and the Black-Scholes option pricing formula, which Fischer Black, an applied mathematician from Harvard, and Myron Scholes, a finance Ph.D. from Chicago, developed.

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However, it also raises the possibility that the causal relationships that determine market movements aren’t fixed, but vary over time. Maybe because of shifts in psychology or government policy, there are periods when markets will settle into a rut, and other periods when they will be apt to gyrate in alarming fashion. This picture seems to jibe with reality, but it raises some tricky issues for quantitative finance. If the underlying reality of the markets is constantly changing, statistical models based on past data will be of limited use, at best, in determining what is likely to happen in the future. And firms and investors that rely on these models to manage risk may well be exposing themselves to danger. The economics profession didn’t exactly embrace Mandelbrot’s criticisms. As the 1970s proceeded, the use of quantitative techniques became increasingly common on Wall Street.

**
The Little Book of Hedge Funds
** by
Anthony Scaramucci

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Andrei Shleifer, asset allocation, Bernie Madoff, business process, carried interest, corporate raider, Credit Default Swap, diversification, diversified portfolio, Donald Trump, Eugene Fama: efficient market hypothesis, fear of failure, fixed income, follow your passion, Gordon Gekko, high net worth, index fund, John Meriwether, Long Term Capital Management, mail merge, margin call, mass immigration, merger arbitrage, money market fund, Myron Scholes, NetJets, Ponzi scheme, profit motive, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk-adjusted returns, risk/return, Ronald Reagan, Saturday Night Live, Sharpe ratio, short selling, Silicon Valley, the new new thing, too big to fail, transaction costs, Vanguard fund, Y2K, Yogi Berra, zero-sum game

If so, run—don’t walk—to another profession. Inside the Mind of a Super Capitalist As Mallaby so keenly reports, “Hedge funds are vehicles for loners and contrarians, for individualists whose ambitions are too big to fit into established financial institutions.” They aren’t the corporate obsequious types. And yet, hedge fund managers come in many different shapes and sizes—from PhDs in quantitative finance (Cliff Asness of AQR Capital Management) to college students trading convertible bonds out of their Ivy League dorm rooms (Ken Griffin of Citadel) to nerdy, mathematical quants (James Simons of Renaissance Technologies) to hyper, passionate, active traders (Daniel Loeb of Third Point). As it would be impossible to define the true essence of a hedge fund manager, below are some interesting—and somewhat humorous—insights into the psychographic portraits of these masters of the universe.

**
Models. Behaving. Badly.: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life
** by
Emanuel Derman

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Albert Einstein, Asian financial crisis, Augustin-Louis Cauchy, Black-Scholes formula, British Empire, Brownian motion, capital asset pricing model, Cepheid variable, creative destruction, crony capitalism, diversified portfolio, Douglas Hofstadter, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Henri Poincaré, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Isaac Newton, law of one price, Mikhail Gorbachev, Myron Scholes, quantitative trading / quantitative ﬁnance, random walk, Richard Feynman, Richard Feynman, riskless arbitrage, savings glut, Schrödinger's Cat, Sharpe ratio, stochastic volatility, the scientific method, washing machines reduced drudgery, yield curve

University of California, Berkeley, Department of Statistics, Technical Report 611, January 2003. 8. In mathematics the symbol A (Delta) before any other symbol indicates an infinitesimally small change in the quantity represented by the second symbol. Thus At is an arbitrarily small increment in time. 9. See Emanuel Derman, “The Perception of Time, Risk and Return During Periods of Speculation,” Quantitative Finance 2, no. 4 (2002): 282–96. 10. “Exclusive” in describing an apartment means desirable and expensive because it excludes many people. In a financial crisis, though, exclusivity means illiquidity. What you want to own in a widespread financial crunch are inclusive securities. 11. I use the Greek letter omega, pronounced “oh-mega,” to quantify deliciousness. 12. F. Black, E. Derman, and W.

**
Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino
** by
Jim McTague

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algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative ﬁnance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

Credit Suisse, a large provider of HFT solutions, hawked algorithms to traders with names like “Guerilla” and “Sniper” to detect big orders in both the public markets and in dark pools, where mutual funds, pensions, and other big buyers and sellers attempt to trade without rippling the markets. A third strategy, called event trading, tried to capitalize on the news of the day and predict which direction the markets would take in reaction to the latest development. Harvey Houtkin used to instruct his trading students, “The trend is your friend,” and this was a variation of that theme. Large quantitative-trading firms such as Medallion engaged heavily in this type of momentum trading. The fourth strategy was old-fashioned arbitrage, in which the traders attempted to find price discrepancies between seemingly unrelated instruments, like stocks and sugar futures, for instance. Generally speaking, the algorithms compared data of past stock and commodities movements to build an understanding of how they might behave in the present.

…

These strategies exacerbated market volatility by driving stocks much higher or lower than they would have moved if investors merely were weighing the underlying fundamentals of the securities. Yaroshevsky saw things occurring in the equities market that had never occurred in the past, like the market beginning to rise robustly during a recession, as it did early in 2009. The rise was driven purely by quantitative trading, he argued. Credit was first extended by the regulators at the Federal Reserve to the bankers and “into the more capable hands of the quantitative geniuses,” he chuckled. Their trading drove the market higher and culminated in the Flash Crash. It wasn’t deliberate market manipulation, in his view. So many Quants employed the same momentum strategies that the market simply became divorced from its fundamentals.

**
Electronic and Algorithmic Trading Technology: The Complete Guide
** by
Kendall Kim

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algorithmic trading, automated trading system, backtesting, commoditize, computerized trading, corporate governance, Credit Default Swap, diversification, en.wikipedia.org, family office, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, money market fund, natural language processing, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, short selling, statistical arbitrage, Steven Levy, transaction costs, yield curve

A new wave of applications that provide full trading suites, such as portfolio modeling, trade blotter, and pre- and post-trade compliance, are being offered by firms such as Tradeware, Portware, Bloomberg, Reuters, and Europeanbased vendors such as Trading Screen. These products, which were once expensive to implement and maintain, are now becoming accessible to new entrants due to price pressure, for example, hedge funds and smaller investment management firms. Portware and FlexTrade are focusing on hedge funds with solutions that allow users to customize quantitative trading strategies alongside traditional risk arbitrage and long/short strategies. As the market for high-priced custom implementation becomes saturated, vendors will shift their focus to midtier asset managers where once only the largest financial firms could justify the expense. More players will implement electronic access to markets integrating trading and portfolio management suites. Total market spending for trading systems was $445 million in 2004, and potentially can reach $701 million in 2007 according to Celent.2 Analysis Identification Placement Routing Bulge Bracket Large Agency Brokers Algo Agency Brokers Data Mgmt OMS Enabler DMA Networks Exhibit 15.2 2 Competitive landscape.

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FlexSIMULATOR Enables clients to build and test trading strategies using real-time and historical tick data. . eFlexTRADER Hosted version of FlexTRADER accessible via the Internet. Sell-side firms can market this product to their own clients to attract additional order flow. Portware Portware is a leading provider of buy-side and sell-side trade and execution management software for basket, single-asset and automated quantitative trading. Portware Professional, its core product, is a centralized platform for trade and execution management. Portware was founded in 2000 and is headquartered in New York, with an office in London. Portware Professional is an order management system, capable of handling both single-asset and portfolio/basket trading with multiuser support. Some of the key features and functionality of Portware Professional include the following: 1.

**
Doing Good Better: How Effective Altruism Can Help You Make a Difference
** by
William MacAskill

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barriers to entry, basic income, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, labour mobility, Lean Startup, M-Pesa, mass immigration, meta analysis, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative ﬁnance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, universal basic income, women in the workforce

Both of these careers also come with a high chance of dropping out, since at each stage, if you fail to be promoted, you’ll probably have to switch into a different job with lower pay. Even taking this into account, however, they’re still among the career paths with the highest expected earnings. Tech entrepreneurship and quantitative trading in hedge funds offer even higher expected earnings, though tech entrepreneurship comes with even higher risks (entrepreneurs have less than a 10 percent chance of ever selling their shares in the company at profit) and quantitative trading requires exceptionally strong mathematical skills. Among less risky careers, medicine is probably the highest-earning option, especially in the United States, though earnings are probably less than in finance. Law is less appealing than one might think, because unless you can get into one of the very top law schools such as Harvard, you won’t likely earn as much as you would in consulting or finance.

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

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

Fundamental quant investing considers many of the same factors as discretionary traders, seeking to buy cheap stocks and short sell expensive ones, but the difference is that fundamental quants do so systematically using computer systems. While discretionary trading has the advantages of a tailored analysis of each trade and the use of soft information such as private conversations, its labor-intensive method implies that only a limited number of securities can be analyzed in depth, and the discretion exposes the trader to psychological biases. Quantitative trading has the advantage of being able to apply a trading idea to thousands of securities around the globe, benefiting from significant diversification. Furthermore, quants can apply their trading ideas with the discipline of a robot. Discipline is important for all traders, but as the saying goes, Have a rule. Always follow the rule, but know when to break it. Even quants sometimes need to “break the rule,” for example, if they realize that there are problems in the data feed or if sudden important events happen that are outside the realm of the models, such as the failure of the investment bank Lehman Brothers in 2008.

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Typically discretionary equity investors buy more stocks than they sell short, but the reverse is true for dedicated short bias hedge funds. Dedicated short bias hedge funds focus on findings stocks that are about to go down, looking for frauds, overstated earnings, or poor business plans. Dedicated short bias hedge funds rely on a fundamental analysis of companies in a similar way to other discretionary equity investors. Discretionary trading can be seen in contrast to quantitative trading, which invests systematically based on a model. Both types of traders may seek lots of data and use valuation models, but whereas discretionary traders make their final trading decisions based on human judgment, quantitative investors trade systematically with minimal human interference. Quantitative investors gather data, check the data, feed it into a model, and let the model send trades to the exchanges.1 Quants try to develop a small edge on each of many small diversified trades using sophisticated processing of ideas that cannot be easily processed using non-quantitative methods.

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In other words, trading is done by feeding data into computers that run various programs with human oversight. Discretionary trading has the advantages of a tailored analysis of each trade and the use of a lot of soft information such as private conversations, but its labor-intensive method implies that only a limited number of securities can be analyzed in depth, and the discretion exposes the trader to psychological biases. Quantitative trading has the advantage of discipline, an ability to apply a trading idea to a wide universe of securities with the benefits of diversification, and efficient portfolio construction, but it must rely only on hard data and the computer program’s limited ability to incorporate real-time judgment. While the three forms of equity investment have several differences, each relies on an understanding of equity valuation.

**
Statistical Arbitrage: Algorithmic Trading Insights and Techniques
** by
Andrew Pole

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algorithmic trading, Benoit Mandelbrot, Chance favours the prepared mind, constrained optimization, Dava Sobel, George Santayana, Long Term Capital Management, Louis Pasteur, mandelbrot fractal, market clearing, market fundamentalism, merger arbitrage, pattern recognition, price discrimination, profit maximization, quantitative trading / quantitative ﬁnance, risk tolerance, Sharpe ratio, statistical arbitrage, statistical model, stochastic volatility, systematic trading, transaction costs

You may begin to see how intervention in the dynamic linear model, exemplified for the EWMA model in Section 3.3, is implemented. The DLM includes ARIMA, EWMA, and regression models as special cases, making it a rich, flexible class with which to work. Monitoring and intervention strategies are readily defined for each model component separately and in combination. See, Pole, et al. for examples. 3.4.3 Volatility Modeling Volatility modeling has an extensive pedigree in quantitative finance. Use in statistical arbitrage is less direct than in derivative valuation where most theoretical development and published applications are seen, but it is nonetheless helpful. Consider just the simple spread modeling that provides much of the background of the discussion in this book: The variance of the return stream determines the richness of potential bets (the basic viability of candidate raw material for a strategy), variability of mark to market gains and losses while a bet is extant (the risk profile of a strategy, stop loss rules), and return stretching by stochastic resonance (see Section 3.7).

**
Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
** by
Joel Hasbrouck

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Alvin Roth, barriers to entry, conceptual framework, correlation coefficient, discrete time, disintermediation, distributed generation, experimental economics, financial intermediation, index arbitrage, information asymmetry, interest rate swap, inventory management, market clearing, market design, market friction, market microstructure, martingale, price discovery process, price discrimination, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, second-price auction, selection bias, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, two-sided market, ultimatum game, zero-sum game

CFA Institute, 2002, Trade Management Guidelines, CFA Institute (formerly the American Institute for Management Research), available online at http://www.cfainstitute.org/standards/pdf/trademgmt_ guidelines.pdf. Chakravarty, Sugato, and Craig W. Holden, 1995, An integrated model of market and limit orders, Journal of Financial Intermediation 4, 213–41. Challet, Damien, and Robin Stinchcombe, 2003, Non-constant rates and over-diffusive prices in a simple model of limit order markets, Quantitative Finance 3, 155–62. Chan, Louis K. C., and Josef Lakonishok, 1993, Institutional trades and intraday stock-price behavior, Journal of Financial Economics 33, 173–99. Chan, Louis K. C., and Josef Lakonishok, 1995, The behavior of stock prices around institutional trades, Journal of Finance 50, 1147–74. Chan, Louis K. C., and Josef Lakonishok, 1997, Institutional equity trading costs: NYSE versus Nasdaq, Journal of Finance 52, 713–35. 185 186 REFERENCES Choi, J.

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Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better
** by
Andrew Palmer

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Affordable Care Act / Obamacare, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, Black-Scholes formula, bonus culture, break the buck, Bretton Woods, call centre, Carmen Reinhart, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Graeber, diversification, diversified portfolio, Edmond Halley, Edward Glaeser, endogenous growth, Eugene Fama: efficient market hypothesis, eurozone crisis, family office, financial deregulation, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, implied volatility, income inequality, index fund, information asymmetry, Innovator's Dilemma, interest rate swap, Kenneth Rogoff, Kickstarter, late fees, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, Mark Zuckerberg, McMansion, money market fund, mortgage debt, mortgage tax deduction, Myron Scholes, negative equity, Network effects, Northern Rock, obamacare, payday loans, peer-to-peer lending, Peter Thiel, principal–agent problem, profit maximization, quantitative trading / quantitative ﬁnance, railway mania, randomized controlled trial, Richard Feynman, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application

. *** WHILE HE WAS STUDYING economics and computer science at Yale University, Paul Gu asked himself the question that eventually confronts every young person: what should he do with his life? The Chinese-born American was keen on the idea of starting his own company, and the most well-trodden route to that future from an East Coast university was first to head to Wall Street and make some money. The obvious choice for a math whiz was a career in quantitative finance: that seemed to offer the right mixture of autonomy, intellectual challenge, and high pay. In the summer of 2011, Gu interned with DE Shaw, an extremely successful algorithmic-trading firm. But something was missing. As much as he might rationally accept that this firm and others like it were adding value to the capital markets, he did not feel an emotional connection to what he was doing.

**
Mathematics for Finance: An Introduction to Financial Engineering
** by
Marek Capinski,
Tomasz Zastawniak

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Black-Scholes formula, Brownian motion, capital asset pricing model, cellular automata, delta neutral, discounted cash flows, discrete time, diversified portfolio, fixed income, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, martingale, quantitative trading / quantitative ﬁnance, random walk, short selling, stochastic process, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

Korn, R. (1997), Optimal Portfolios, World Scientiﬁc, Singapore. Lamberton, D. and Lapeyre, B. (1996), Introduction to Stochastic Calculus Applied to Finance, Chapman and Hall, London. Musiela, M. and Rutkowski, M. (1997), Martingale Methods in Financial Modelling, Springer-Verlag, Berlin. Pliska, S. R. (1997), Introduction to Mathematical Finance: Discrete Time Models, Blackwell, Maldon, Mass. Wilmott, P. (2001), Paul Wilmott Introduces Quantitative Finance, John Wiley & Sons, Chichester. Wilmott, P., Howison, S. and Dewynne, J. (1995), The Mathematics of Financial Derivatives: A Student Introduction, Cambridge University Press, Cambridge. Glossary of Symbols A B β c C C CA CE E C Cov delta div div0 D D DA E E∗ f F gamma Φ k K i m ﬁxed income (risk free) security price; money market account bond price beta factor covariance call price; coupon value covariance matrix American call price European call price discounted European call price covariance Greek parameter delta dividend present value of dividends derivative security price; duration discounted derivative security price price of an American type derivative security expectation risk-neutral expectation futures price; payoﬀ of an option; forward rate forward price; future value; face value Greek parameter gamma cumulative binomial distribution logarithmic return return coupon rate compounding frequency; expected logarithmic return 305 306 Mathematics for Finance M m µ N N k ω Ω p p∗ P PA PE P E PA r rdiv re rF rho ρ S S σ t T τ theta u V Var VaR vega w w W x X y z market portfolio expected returns as a row matrix expected return cumulative normal distribution the number of k-element combinations out of N elements scenario probability space branching probability in a binomial tree risk-neutral probability put price; principal American put price European put price discounted European put price present value factor of an annuity interest rate dividend yield eﬀective rate risk-free return Greek parameter rho correlation risky security (stock) price discounted risky security (stock) price standard deviation; risk; volatility current time maturity time; expiry time; exercise time; delivery time time step Greek parameter theta row matrix with all entries 1 portfolio value; forward contract value, futures contract value variance value at risk Greek parameter vega symmetric random walk; weights in a portfolio weights in a portfolio as a row matrix Wiener process, Brownian motion position in a risky security strike price position in a ﬁxed income (risk free) security; yield of a bond position in a derivative security Index admissible – portfolio 5 – strategy 79, 88 American – call option 147 – derivative security – put option 147 amortised loan 30 annuity 29 arbitrage 7 at the money 169 attainable – portfolio 107 – set 107 183 basis – of a forward contract 128 – of a futures contract 140 basis point 218 bear spread 208 beta factor 121 binomial – distribution 57, 180 – tree model 7, 55, 81, 174, 238 Black–Derman–Toy model 260 Black–Scholes – equation 198 – formula 188 bond – at par 42, 249 – callable 255 – face value 39 – ﬁxed-coupon 255 – ﬂoating-coupon 255 – maturity date 39 – stripped 230 – unit 39 – with coupons 41 – zero-coupon 39 Brownian motion 69 bull spread 208 butterﬂy 208 – reversed 209 call option 13, 181 – American 147 – European 147, 188 callable bond 255 cap 258 Capital Asset Pricing Model 118 capital market line 118 caplet 258 CAPM 118 Central Limit Theorem 70 characteristic line 120 compounding – continuous 32 – discrete 25 – equivalent 36 – periodic 25 – preferable 36 conditional expectation 62 contingent claim 18, 85, 148 – American 183 – European 173 continuous compounding 32 continuous time limit 66 correlation coeﬃcient 99 coupon bond 41 coupon rate 249 307 308 covariance matrix 107 Cox–Ingersoll–Ross model 260 Cox–Ross–Rubinstein formula 181 cum-dividend price 292 delta 174, 192, 193, 197 delta hedging 192 delta neutral portfolio 192 delta-gamma hedging 199 delta-gamma neutral portfolio 198 delta-vega hedging 200 delta-vega neutral portfolio 198 derivative security 18, 85, 253 – American 183 – European 173 discount factor 24, 27, 33 discounted stock price 63 discounted value 24, 27 discrete compounding 25 distribution – binomial 57, 180 – log normal 71, 186 – normal 70, 186 diversiﬁable risk 122 dividend yield 131 divisibility 4, 74, 76, 87 duration 222 dynamic hedging 226 eﬀective rate 36 eﬃcient – frontier 115 – portfolio 115 equivalent compounding 36 European – call option 147, 181, 188 – derivative security 173 – put option 147, 181, 189 ex-coupon price 248 ex-dividend price 292 exercise – price 13, 147 – time 13, 147 expected return 10, 53, 97, 108 expiry time 147 face value 39 ﬁxed interest 255 ﬁxed-coupon bond 255 ﬂat term structure 229 ﬂoating interest 255 ﬂoating-coupon bond 255 ﬂoor 259 ﬂoorlet 259 Mathematics for Finance forward – contract 11, 125 – price 11, 125 – rate 233 fundamental theorem of asset pricing 83, 88 future value 22, 25 futures – contract 134 – price 134 gamma 197 Girsanov theorem 187 Greek parameters 197 growth factor 22, 25, 32 Heath–Jarrow–Morton model hedging – delta 192 – delta-gamma 199 – delta-vega 200 – dynamic 226 in the money 169 initial – forward rate 232 – margin 135 – term structure 229 instantaneous forward rate interest – compounded 25, 32 – ﬁxed 255 – ﬂoating 255 – simple 22 – variable 255 interest rate 22 interest rate option 254 interest rate swap 255 261 233 LIBID 232 LIBOR 232 line of best ﬁt 120 liquidity 4, 74, 77, 87 log normal distribution 71, 186 logarithmic return 34, 52 long forward position 11, 125 maintenance margin 135 margin call 135 market portfolio 119 market price of risk 212 marking to market 134 Markowitz bullet 113 martingale 63, 83 Index 309 martingale probability 63, 250 maturity date 39 minimum variance – line 109 – portfolio 108 money market 43, 235 no-arbitrage principle 7, 79, 88 normal distribution 70, 186 option – American 183 – at the money 169 – call 13, 147, 181, 188 – European 173, 181 – in the money 169 – interest rate 254 – intrinsic value 169 – out of the money 169 – payoﬀ 173 – put 18, 147, 181, 189 – time value 170 out of the money 169 par, bond trading at 42, 249 payoﬀ 148, 173 periodic compounding 25 perpetuity 24, 30 portfolio 76, 87 – admissible 5 – attainable 107 – delta neutral 192 – delta-gamma neutral 198 – delta-vega neutral 198 – expected return 108 – market 119 – variance 108 – vega neutral 197 positive part 148 predictable strategy 77, 88 preferable compounding 36 present value 24, 27 principal 22 put option 18, 181 – American 147 – European 147, 189 put-call parity 150 – estimates 153 random interest rates random walk 67 rate – coupon 249 – eﬀective 36 237 – forward 233 – – initial 232 – – instantaneous 233 – of interest 22 – of return 1, 49 – spot 229 regression line 120 residual random variable 121 residual variance 122 return 1, 49 – expected 53 – including dividends 50 – logarithmic 34, 52 reversed butterﬂy 209 rho 197 risk 10, 91 – diversiﬁable 122 – market price of 212 – systematic 122 – undiversiﬁable 122 risk premium 119, 123 risk-neutral – expectation 60, 83 – market 60 – probability 60, 83, 250 scenario 47 security market line 123 self-ﬁnancing strategy 76, 88 short forward position 11, 125 short rate 235 short selling 5, 74, 77, 87 simple interest 22 spot rate 229 Standard and Poor Index 141 state 238 stochastic calculus 71, 185 stochastic diﬀerential equation 71 stock index 141 stock price 47 strategy 76, 87 – admissible 79, 88 – predictable 77, 88 – self-ﬁnancing 76, 88 – value of 76, 87 strike price 13, 147 stripped bond 230 swap 256 swaption 258 systematic risk 122 term structure 229 theta 197 time value of money 21 310 trinomial tree model Mathematics for Finance 64 underlying 85, 147 undiversiﬁable risk 122 unit bond 39 value at risk 202 value of a portfolio 2 value of a strategy 76, 87 VaR 202 variable interest 255 Vasiček model 260 vega 197 vega neutral portfolio volatility 71 weights in a portfolio Wiener process 69 yield 216 yield to maturity 229 zero-coupon bond 39 197 94

**
Bad Data Handbook
** by
Q. Ethan McCallum

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Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

Jacob Perkins is the CTO of Weotta, a NLTK contributer, and the author of Python Text Processing with NLTK Cookbook. He also created the NLTK demo and API site text-processing.com, and periodically blogs at streamhacker.com. In a previous life, he invented the refrigerator. Spencer Burns is a data scientist/engineer living in San Francisco. He has spent the past 15 years extracting information from messy data in fields ranging from intelligence to quantitative finance to social media. Richard Cotton is a data scientist with a background in chemical health and safety, and has worked extensively on tools to give non-technical users access to statistical models. He is the author of the R packages “assertive” for checking the state of your variables and “sig” to make sure your functions have a sensible API. He runs The Damned Liars statistics consultancy.

**
Five Billion Years of Solitude: The Search for Life Among the Stars
** by
Lee Billings

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Albert Einstein, Arthur Eddington, California gold rush, Colonization of Mars, cosmological principle, cuban missile crisis, dark matter, Dava Sobel, double helix, Edmond Halley, full employment, hydraulic fracturing, index card, Isaac Newton, Kuiper Belt, Magellanic Cloud, music of the spheres, out of africa, Peter H. Diamandis: Planetary Resources, planetary scale, profit motive, quantitative trading / quantitative ﬁnance, Ralph Waldo Emerson, RAND corporation, random walk, Search for Extraterrestrial Intelligence, Searching for Interstellar Communications, selection bias, Silicon Valley, Solar eclipse in 1919, technological singularity, the scientific method, transcontinental railway

To do that, he had needed to understand supply and demand—the cost of building new mines, the manner in which metals were extracted and used—so he had educated himself in the geology of ore-forming bodies, the same geology that so long ago put gold in California’s hills. Laughlin had reveled in using his skills in a technical field with direct commercial applications. Astronomy was a technical field, he acknowledged, but for as long as humans remained a single-planet, single-star species, it would be disconnected from the profits associated with semiconductor physics, petroleum prospecting, or quantitative finance. His new investigations had borne strange fruit: he had become preoccupied with what certain obscure market trends revealed about the nature of prediction and the formation of monetary value, and had begun to closely scrutinize various trades as they propagated through the global financial system. Viewing the scintillating patterns of trades, his “bird’s-eye view” on what would otherwise be “a mostly hidden world,” Laughlin had begun feeling the familiar effervescence once again.

**
The End of Accounting and the Path Forward for Investors and Managers (Wiley Finance)
** by
Feng Gu

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active measures, Affordable Care Act / Obamacare, barriers to entry, business process, Claude Shannon: information theory, Clayton Christensen, commoditize, conceptual framework, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, double entry bookkeeping, Exxon Valdez, financial innovation, fixed income, hydraulic fracturing, index fund, information asymmetry, intangible asset, inventory management, Joseph Schumpeter, Kenneth Arrow, knowledge economy, moral hazard, new economy, obamacare, quantitative easing, quantitative trading / quantitative ﬁnance, QWERTY keyboard, race to the bottom, risk/return, Robert Shiller, Robert Shiller, shareholder value, Steve Jobs, The Great Moderation, value at risk

On the other hand, a research on Singaporean companies found: “ . . . mandatory quarterly reporting does not reduce information asymmetry . . . ” (Peter Kajüter, Florian Klassman, and Martin Nienhaus, Causal Effects of Quarterly Reporting—An Analysis of Benefits and Costs, working paper (University of Muenster, Abstract, 2015).) Also, a study on monthly disclosures documents no positive effects on transparency of such frequent reports (Andrew Van Buskirk, “Disclosure 212 PRACTICAL MATTERS Frequency and Information Asymmetry,” Review of Quantitative Finance and Accounting, 38 (2012): 411–440). 17. However, we didn’t include in our questionnaire the requirement to report quarterly sales and cost of sales. We just asked about total elimination of quarterly reporting. 18. In the spirit of “natural experiments,” which certain governments and institutions experiment with, the SEC could abolish quarterly reporting for a few industries only and observe the consequences over several years, before applying it to all companies. 19.

**
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
** by
Nassim Nicholas Taleb

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Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, fixed income, global village, hindsight bias, Kenneth Arrow, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, Myron Scholes, Paul Samuelson, quantitative trading / quantitative ﬁnance, QWERTY keyboard, random walk, Richard Feynman, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Turing test, Yogi Berra

He also had other interesting attributes (he blew up trading his account after experiencing excessive opulence—people’s understanding of probability does not translate into their behavior). The reader can guess that the next step from such probabilistic introspection is to get drawn into philosophy, particularly the branch of philosophy that concerns itself with knowledge, called epistemology or methodology, or philosophy of science. We will not get into the topic until later in the book. FUN IN MY ATTIC Making History In the early 1990s, like many of my friends in quantitative finance, I became addicted to the various Monte Carlo engines, which I taught myself to build, thrilled to feel that I was generating history, a Demiurgus. It can be electrifying to generate virtual histories and watch the dispersion between the various results. Such dispersion is indicative of the degree of resistance to randomness. This is where I am convinced that I have been extremely lucky in my choice of career: One of the attractive aspects of my profession as a quantitative option trader is that I have close to 95% of my day free to think, read, and research (or “reflect” in the gym, on ski slopes, or, more effectively, on a park bench).

pages: 360 words: 85,321

**
The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling
** by
Adam Kucharski

Ada Lovelace, Albert Einstein, Antoine Gombaud: Chevalier de Méré, beat the dealer, Benoit Mandelbrot, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, correlation does not imply causation, diversification, Edward Lorenz: Chaos theory, Edward Thorp, Everything should be made as simple as possible, Flash crash, Gerolamo Cardano, Henri Poincaré, Hibernia Atlantic: Project Express, if you build it, they will come, invention of the telegraph, Isaac Newton, John Nash: game theory, John von Neumann, locking in a profit, Louis Pasteur, Nash equilibrium, Norbert Wiener, p-value, performance metric, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative ﬁnance, random walk, Richard Feynman, Richard Feynman, Ronald Reagan, Rubik’s Cube, statistical model, The Design of Experiments, Watson beat the top human players on Jeopardy!, zero-sum game

Journal of Quantitative Analysis in Sports 2, no. 1 (2011). 106researchers at Smartodds and the University of Salford: McHale and Szczepański, “Mixed Effects Model.” 107Erroneous odds are less common: Author interview with David Hastie, March 2013. 107“I would start with the minor sports”: Predictive Sports Betting, MIT Sloan Sports Analytics Conference. CHAPTER 5 109“What hath God wrought!”: History of the telegram comes from: “The Birth of Electrical Communications—1837.” University of Salford. http://www.cntr.salford.ac.uk/comms/ebirth.php. 110traders used telegrams to tell each other: Poitras, Geoffrey. “Arbitrage: Historical Perspectives.” Encyclopedia of Quantitative Finance, 2010. doi:10.1002/9780470061602.eqf01010. 110traders refer to the GBP/USD exchange rate: Author experience. 110Some would even trek further afield: Poitras, “Arbitrage: Historical Perspectives.” 111researchers at Athens University looked at bookmakers’ odds: Vlastakis, Nikolaos, George Dotsis, and Raphael N. Markellos. “How Efficient Is the European Football Betting Market? Evidence from Arbitrage and Trading Strategies.”

pages: 293 words: 88,490

**
The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction
** by
Richard Bookstaber

asset allocation, bank run, bitcoin, butterfly effect, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative ﬁnance, railway mania, Ralph Waldo Emerson, Richard Feynman, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve

Arthur, W. Brian. 1999. “Complexity and the Economy.” Science 284, no. 5411: 107–9. doi: 10.1126/science.284.5411.107. Bacher, Rainer, and Urs Näf. 2003. “Report on the Blackout in Italy on 28 September 2003.” Swiss Federal Office of Energy. Bargigli, Leonardo, Giovanni Di Iasio, Luigi Infante, Fabrizio Lillo, and Federico Pierobon. 2015. “The Multiplex Structure of Interbank Networks.” Quantitative Finance 15, no. 4: 673–91. doi: 10.1080/14697688.2014.968356. Becker, Gary S. 1976. The Economic Approach to Human Behavior. Chicago: University of Chicago Press. Beinhocker, Eric D. 2006. The Origins of Wealth: Evolution, Complexity, and the Radical Remaking of Economics. Boston: Harvard Business School Press. ———. 2013. “Reflexivity, Complexity, and the Nature of Social Science.” Journal of Economic Methodology 20, no. 4: 330–42.

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

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Albert Einstein, Bernie Madoff, Black Swan, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative ﬁnance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

Longstreet, Roy. Viewpoints of a Commodity Trader. Greenville: Traders Press, 1967. Mallaby, Sebastian. More Money than God: Hedge Funds and the Making of a New Elite. New York: The Penguin Press, 2010. Mauboussin, Michael. More Than You Know: Finding Financial Wisdom in Unconventional Places. New York: Columbia Business School, 2006. Narang, Rishi. Inside the Black Box: The Simple Truth About Quantitative Trading. Hoboken: John Wiley and Sons, Inc., 2009. Neill, Humphrey. Tape Reading: Market and Tactics. LaVergne: BN Publishing, 2008. O’Shaughnessy, James. What Works on Wall Street: A Guide to the BestPerforming Investment Strategies of all Time. New York: McGraw Hill, 1997. Patel, Charles. Technical Trading Systems for Commodities and Stocks. Greenville: Traders Press, Inc., 1998. Paulos, John Allen.

**
All the Devils Are Here
** by
Bethany McLean

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Asian financial crisis, asset-backed security, bank run, Black-Scholes formula, break the buck, call centre, collateralized debt obligation, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Exxon Valdez, fear of failure, financial innovation, fixed income, high net worth, Home mortgage interest deduction, interest rate swap, laissez-faire capitalism, Long Term Capital Management, margin call, market bubble, market fundamentalism, Maui Hawaii, money market fund, moral hazard, mortgage debt, Northern Rock, Own Your Own Home, Ponzi scheme, quantitative trading / quantitative ﬁnance, race to the bottom, risk/return, Ronald Reagan, Rosa Parks, shareholder value, short selling, South Sea Bubble, statistical model, telemarketer, too big to fail, value at risk, zero-sum game

Morgan’s risk model could be adapted to bestow coveted triple-A ratings on large chunks of complex new products created out of subprime mortgages. Firms could use VaR to persuade regulators—and themselves—that they were taking on very little risk, even as they were loading up on subprime securities. And they could use credit default swaps to off-load their own subprime risks onto some other entity willing to accept it. By the early 2000s, these two worlds—subprime and quantitative finance—were completely intertwined. Not that anyone at J.P. Morgan could see what was coming. Like Ranieri in the 1980s, the bank’s eager young innovators were convinced they were making the financial world a better, safer world. But they weren’t. The chairman and CEO of J.P. Morgan in the early 1990s was a calm, unflappable British expatriate named Sir Dennis Weatherstone. Knighted in 1990, the year he took over the bank, Weatherstone had the bearing of a patrician despite working-class roots; his first job, at the age of sixteen, was as a book-keeper in the London office of a firm J.P.

**
Other People's Money: Masters of the Universe or Servants of the People?
** by
John Kay

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Affordable Care Act / Obamacare, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, call centre, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, corporate governance, Credit Default Swap, cross-subsidies, dematerialisation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, financial thriller, fixed income, Flash crash, forward guidance, Fractional reserve banking, full employment, George Akerlof, German hyperinflation, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, income inequality, index fund, inflation targeting, information asymmetry, intangible asset, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, John Meriwether, light touch regulation, London Whale, Long Term Capital Management, loose coupling, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, money market fund, moral hazard, mortgage debt, Myron Scholes, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, Paul Samuelson, peer-to-peer lending, performance metric, Peter Thiel, Piper Alpha, Ponzi scheme, price mechanism, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, railway mania, Ralph Waldo Emerson, random walk, regulatory arbitrage, Renaissance Technologies, rent control, Richard Feynman, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, Schrödinger's Cat, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, sovereign wealth fund, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, Steve Wozniak, The Great Moderation, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tobin tax, too big to fail, transaction costs, tulip mania, Upton Sinclair, Vanguard fund, Washington Consensus, We are the 99%, Yom Kippur War

This revolution in the technology of finance was matched by – indeed was only possible because of – the parallel revolution in information technology. When trading in financial futures began, the Chicago Mercantile Exchange still centred on ‘the pit’, in which aggressive traders shouted offers as they elbowed deals away from their colleagues. Today every trader has a screen. The Black–Scholes model, and the many techniques of quantitative finance that came out of Chicago and elsewhere, could not have been widely applied without the power of modern computers. Regulation also promoted the growth of a trading culture. The growth of the Eurodollar market demonstrated that regulatory anomalies could be used by banks to attract business. And by countries. Governments that promoted the interests of these banks could make regulatory arbitrage easier.

**
Commodity Trading Advisors: Risk, Performance Analysis, and Selection
** by
Greg N. Gregoriou,
Vassilios Karavas,
François-Serge Lhabitant,
Fabrice Douglas Rouah

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Asian financial crisis, asset allocation, backtesting, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, fixed income, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Ponzi scheme, quantitative trading / quantitative ﬁnance, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, technology bubble, transaction costs, value at risk, zero-sum game

He received his Ph.D. in Economics from Wayne State University in 1990. Dr. Christopherson is a coeditor and contributing author of The Virtuous Vice: Globalization, published by Praeger in 2004, and has numerous articles, papers, and book reviews to his credit appearing in journals, books, and trade publications. Gwenevere Darling holds a B.S. in Actuarial Mathematics and Management Engineering with a concentration in Quantitative Finance from Worcester Polytechnic Institute. Fernando Diz is the Whitman Associate Professor of Finance at the Syracuse University Martin J. Whitman School of Management. He also has been Visiting Associate Professor of Finance at the Johnson Graduate School of Management, Cornell University, where he taught courses on derivatives and financial engineering. Professor Diz is also the Founder and President of M&E Financial Markets Research, LLC.

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The Default Line: The Inside Story of People, Banks and Entire Nations on the Edge
** by
Faisal Islam

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Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, Basel III, Ben Bernanke: helicopter money, Berlin Wall, Big bang: deregulation of the City of London, British Empire, capital controls, carbon footprint, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, credit crunch, Credit Default Swap, crony capitalism, dark matter, deindustrialization, Deng Xiaoping, disintermediation, energy security, Eugene Fama: efficient market hypothesis, eurozone crisis, financial deregulation, financial innovation, financial repression, floating exchange rates, forensic accounting, forward guidance, full employment, G4S, ghettoisation, global rebalancing, global reserve currency, hiring and firing, inflation targeting, Irish property bubble, Just-in-time delivery, labour market flexibility, light touch regulation, London Whale, Long Term Capital Management, margin call, market clearing, megacity, Mikhail Gorbachev, mini-job, mittelstand, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, North Sea oil, Northern Rock, offshore financial centre, open economy, paradox of thrift, Pearl River Delta, pension reform, price mechanism, price stability, profit motive, quantitative easing, quantitative trading / quantitative ﬁnance, race to the bottom, regulatory arbitrage, reserve currency, reshoring, Right to Buy, rising living standards, Ronald Reagan, savings glut, shareholder value, sovereign wealth fund, The Chicago School, the payments system, too big to fail, trade route, transaction costs, two tier labour market, unorthodox policies, uranium enrichment, urban planning, value at risk, working-age population, zero-sum game

Taleb told me during the crisis that he had been trying to persuade the King of Sweden to rescind the Nobel Prize for Economics, on account of the damage done by its winners to mankind. To the extent that the world’s central bankers can understand what Taleb is saying, they are listening. Anand Sinha, the deputy governor of the Reserve Bank of India, puts it best when he says that economists have ‘physics envy’. ‘The mistake has been in elevating quantitative finance to the status of physics,’ Sinha mused in Mumbai, in a speech that drew on the work of Taleb’s collaborator Pablo Triana. ‘Physics deals with the laws of nature governing the universe. The objects have unique physical attributes (i.e. position, velocity, temperature, etc.) and the universe evolves according to the immutable laws of nature. Any observation or measurement of physical attributes does not change them, or even if it does, it does so in a predictable way.

**
Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
** by
David Aronson

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Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, capital asset pricing model, cognitive dissonance, compound rate of return, computerized trading, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative ﬁnance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

An asset-class benchmark measures the returns earned and risks incurred by investing in a speciﬁc asset class, with no management skill. Lars Kestner, Quantitative Trading Strategies: Harnessing the Power of Quantitative Techniques to Create a Winning Trading Program (New York: McGraw-Hill, 2003), 129–180. The eight market sectors tested were foreign exchange, interest rates, stock index, metals, energy, grains, meats, and softs. The nine industry sectors were energy, basic materials, consumer discretionary, consumer staples, health care, ﬁnancials and information technology, telecom. The three stock indexes were S&P 500, NASDAQ 100, and Russell 2000. The ﬁve trend-following systems were channel breakout, dual moving-average crossover, two version of momentum, and MACD versus its signal line. For more description see Kestner’s Quantitative Trading Strategies. M. Cooper, “Filter Rules Based on Price and Volume in Individual Security Overreaction,” Review of Financial Studies 12, no. 4 (Special 1999), 901–935.

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Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
** by
Thomas H. Davenport,
Julia Kirby

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AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Khan Academy, knowledge worker, labor-force participation, lifelogging, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Richard Feynman, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

The process we’re describing, of machines taking the high-end cognitive parts of work and turning people into a sort of human user interface, is occurring across many professional realms. Actual decision-making roles have been ceded to computers—and they are doing pretty well in those roles, despite some occasional hiccups. “Program trading” (also known as high-frequency, algorithmic, or quantitative trading) of equities and fixed-income investments, for example, is widespread on Wall Street and around the financial world. It’s one of the reasons why the New York Stock Exchange is so quiet today. Decisions about which stocks and bonds to buy for what price used to be made by human traders but are now largely made by computer. Likewise, decisions that used to be made by human pricing analysts are now arrived at automatically.

**
Market Sense and Nonsense
** by
Jack D. Schwager

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3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

I would like to thank Daniel Stark for providing me access to Stark & Company’s very comprehensive commodity trading advisor (CTA) database that, critically, also contains returns for defunct funds—data that is essential in conducting statistically unbiased analysis of the relationship between past and future returns. About the Author Jack D. Schwager is a recognized industry expert in futures and hedge funds and the author of a number of widely acclaimed financial books. He is currently the co–portfolio manager for the ADM Investor Services Diversified Strategies Fund, a portfolio of futures and foreign exchange (FX) managed accounts. He is also an adviser to Marketopper, an India-based quantitative trading firm, supervising a major project that will adapt that firm’s trading technology to trade a global futures portfolio. Previously, Mr. Schwager was a partner in the Fortune Group, a London-based hedge fund advisory firm acquired by the Close Brothers Group. His previous experience also includes 22 years as director of futures research for some of Wall Street’s leading firms and 10 years as the co-principal of a CTA.

**
The Big Short: Inside the Doomsday Machine
** by
Michael Lewis

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Asperger Syndrome, asset-backed security, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversified portfolio, facts on the ground, financial innovation, fixed income, forensic accounting, Gordon Gekko, high net worth, housing crisis, illegal immigration, income inequality, index fund, interest rate swap, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, medical residency, money market fund, moral hazard, mortgage debt, pets.com, Ponzi scheme, Potemkin village, quantitative trading / quantitative ﬁnance, Robert Bork, short selling, Silicon Valley, the new new thing, too big to fail, value at risk, Vanguard fund, zero-sum game

The longest options available to individual investors on public exchanges were LEAPs, which were two-and-a-half-year options on common stocks. You know, Ben said to Charlie and Jamie, if you established yourself as a serious institutional investor, you could phone up Lehman Brothers or Morgan Stanley and buy eight-year options on whatever you wanted. Would you like that? They would! They wanted badly to be able to deal directly with the source of what they viewed as the most underpriced options: the most sophisticated, quantitative trading desks at Goldman Sachs, Deutsche Bank, Bear Stearns, and the rest. The hunting license, they called it. The hunting license had a name: an ISDA. They were the same agreements, dreamed up by the International Swaps and Derivatives Association, that Mike Burry secured before he bought his first credit default swaps. If you got your ISDA, you could in theory trade with the big Wall Street firms, if not as an equal then at least as a grown-up.

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A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation
** by
Richard Bookstaber

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affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, Black Swan, Black-Scholes formula, Bonfire of the Vanities, butterfly effect, commoditize, commodity trading advisor, computer age, computerized trading, disintermediation, diversification, double entry bookkeeping, Edward Lorenz: Chaos theory, Edward Thorp, family office, financial innovation, fixed income, frictionless, frictionless market, George Akerlof, implied volatility, index arbitrage, intangible asset, Jeff Bezos, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, loose coupling, margin call, market bubble, market design, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shock, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk tolerance, risk/return, Robert Shiller, Robert Shiller, rolodex, Saturday Night Live, selection bias, shareholder value, short selling, Silicon Valley, statistical arbitrage, The Market for Lemons, time value of money, too big to fail, transaction costs, tulip mania, uranium enrichment, William Langewiesche, yield curve, zero-coupon bond, zero-sum game

ZBI traded only with internal funds—those of the Ziff family, of publishing fame—in what is termed in the investment world a family office; it was never chasing after other hedge funds’ investor dollars. Not long after I joined ZBI I moved from risk management to portfolio management. I redirected the efforts of a small group of PhDs who had been providing quantitative analysis for the traditional portfolio managers toward running an internal portfolio based on quantitative trading models. While the trading center for Moore was macro strategies, at ZBI the center was equities, and the portfolio I managed was an equity portfolio. So between this and Scribe Reports, I had moved solidly into the world of equity hedge funds, and I found equities to be a very attractive market. There are many state variables that underlie the price of a stock, and with years of data on thousands of stocks there is a statistical soup of observations where relationships can be coaxed out in many dimensions (although this bounty is not necessarily an advantage—for those without sufficient discipline or statistical knowledge, there is enough data to find just about any relationship you want).

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Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness
** by
Frederic Laloux,
Ken Wilber

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Albert Einstein, augmented reality, blue-collar work, Buckminster Fuller, call centre, carbon footprint, conceptual framework, corporate social responsibility, crowdsourcing, failed state, future of work, hiring and firing, index card, interchangeable parts, invisible hand, job satisfaction, Johann Wolfgang von Goethe, Kenneth Rogoff, meta analysis, meta-analysis, pattern recognition, post-industrial society, quantitative trading / quantitative ﬁnance, randomized controlled trial, selection bias, shareholder value, Silicon Valley, the market place, the scientific method, Tony Hsieh, zero-sum game

There is a school of thought that suggests we need accounting systems that track not just profit but also a firm’s impact on people and the planet; how else could managers make trade-offs between these elements? The argument sounds reasonable, so how come none of the pioneer Teal Organizations use multiple-bottom-line accounting systems? I think the following is at play: multiple bottom lines may help to overcome the fixation on profits alone, but the concept is still rooted in Orange thinking, where decisions are informed only by quantitative trade-offs, by weighing costs and benefits. From an Evolutionary-Teal perspective, not everything needs to be quantified to discern a right course of action. Of course, there are valuable insights to be gained from measuring how a company’s actions impact the environment and society (and for that reason, multiple bottom lines may well become a standard way of reporting in the future). But these pioneers seem to believe that, more than advanced accounting systems, we need integrity and wholeness to transcend the primacy of profits and heal our relationship with the world.

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The Extended Phenotype: The Long Reach of the Gene
** by
Richard Dawkins

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Alfred Russel Wallace, assortative mating, Douglas Hofstadter, Drosophila, epigenetics, Gödel, Escher, Bach, impulse control, Menlo Park, Necker cube, p-value, phenotype, quantitative trading / quantitative ﬁnance, selection bias, stem cell

If such a gene happened to cause, say, malfunction of the liver, that would be just too bad; the gene would increase anyway, since selection for good health is much less effective than selection by competition among sperm cells’ (Crow 1979). There is, of course, no particular reason why a sperm competition gene should happen to cause malfunction of the liver but, as already pointed out, most mutations are deleterious, so some undesirable side effect is pretty likely. Why does Crow assert that selection for good health is much less effective than selection by competition among sperm cells? There must inevitably be a quantitative trade-off involving the magnitude of the effect on health. But, that aside, and even allowing for the controversial possibility that only a minority of sperms are viable (Cohen 1977), the argument appears to have force because the competition between sperm cells in an ejaculate would seem to be so fierce. A million million spermatozoa, All of them alive: Out of their cataclysm but one poor Noah Dare hope to survive.

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Making Globalization Work
** by
Joseph E. Stiglitz

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affirmative action, Andrei Shleifer, Asian financial crisis, banking crisis, barriers to entry, Berlin Wall, business process, capital controls, central bank independence, corporate governance, corporate social responsibility, currency manipulation / currency intervention, Doha Development Round, Exxon Valdez, Fall of the Berlin Wall, Firefox, full employment, Gini coefficient, global reserve currency, Gunnar Myrdal, happiness index / gross national happiness, illegal immigration, income inequality, income per capita, incomplete markets, Indoor air pollution, informal economy, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), inventory management, invisible hand, John Markoff, Kenneth Arrow, Kenneth Rogoff, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, microcredit, moral hazard, North Sea oil, offshore financial centre, oil rush, open borders, open economy, price stability, profit maximization, purchasing power parity, quantitative trading / quantitative ﬁnance, race to the bottom, reserve currency, rising living standards, risk tolerance, Silicon Valley, special drawing rights, statistical model, the market place, The Wealth of Nations by Adam Smith, Thomas L Friedman, trade liberalization, trickle-down economics, union organizing, Washington Consensus, zero-sum game

Even putting this statistical debate aside, it is striking that even NAFTA advocates suggest that it has had at most a small effect on growth, even in a period in which, because of the Mexican crisis, trade was vital. Mexico's joining the WTO in January 1995 may have made more of a difference in some respects than NAFTA, because it limited what the government could do in the aftermath of the 1994-95 crisis. (In earlier crises, the government had imposed numerous quantitative trade restrictions, which critics say had longlasting adverse effects.). NAFTA proponents sometimes argue that NAETA's real contribution was opening up investment, not trade. But, critics say, while the effect on overall foreign investment is uncertain, some aspects of foreign investment may have contributed to Mexico's slow growth. As international banks took over all but one of Mexico's banks—acquisitions that NAFTA effectively encouraged—the supply of credit to small- and medium-sized domestic enterprises became constrained, and growth (outside firms linked with international exports) diminished.

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The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
** by
Kariappa Bheemaiah

accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, distributed ledger, diversification, double entry bookkeeping, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, labour market flexibility, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus

His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. During his career, he has made important contributions to complex systems (See Appendix 1), chaos theory, artificial life, theoretical biology, time series forecasting and Econophysics. He is also an entrepreneur and co-founded the Prediction Company, one of the first companies to do fully automated quantitative trading. 30 Jacky Mallett has a PhD in computer science from MIT. She is a research scientist at Reykjavik Universit, who works on the design and analysis of high performance, distributed computing systems and simulations of economic systems with a focus on Basel regulatory framework for banks, and its macro-economic implications. She is also the creator of ‘Threadneedle’, an experimental tool for simulating fractional reserve banking systems. 29 203 Chapter 4 ■ Complexity Economics: A New Way to Witness Capitalism The question about [modelling] individual households raises a very significant issue: are there distinctions at that level that could affect the macro-economy?

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Thinking, Fast and Slow
** by
Daniel Kahneman

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Albert Einstein, Atul Gawande, availability heuristic, Bayesian statistics, Black Swan, Cass Sunstein, Checklist Manifesto, choice architecture, cognitive bias, complexity theory, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, delayed gratification, demand response, endowment effect, experimental economics, experimental subject, Exxon Valdez, feminist movement, framing effect, hindsight bias, index card, information asymmetry, job satisfaction, John von Neumann, Kenneth Arrow, libertarian paternalism, loss aversion, medical residency, mental accounting, meta analysis, meta-analysis, nudge unit, pattern recognition, Paul Samuelson, pre–internet, price anchoring, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, risk tolerance, Robert Metcalfe, Ronald Reagan, The Chicago School, The Wisdom of Crowds, Thomas Bayes, transaction costs, union organizing, Walter Mischel, Yom Kippur War

Greer, “Anticipatory Affect: Neural Correlates and Consequences for Choice,” Philosophical Transactions of the Royal Society B 363 (2008): 3771–86. riskless and risky decisions: A review of the price of risk, based on “international data from 16 different countries during over 100 years,” yielded an estimate of 2.3, “in striking agreement with estimates obtained in the very different methodology of laboratory experiments of individual decision-making”: Moshe Levy, “Loss Aversion and the Price of Risk,” Quantitative Finance 10 (2010): 1009–22. effect of price increases: Miles O. Bidwel, Bruce X. Wang, and J. Douglas Zona, “An Analysis of Asymmetric Demand Response to Price Changes: The Case of Local Telephone Calls,” Journal of Regulatory Economics 8 (1995): 285–98. Bruce G. S. Hardie, Eric J. Johnson, and Peter S. Fader, “Modeling Loss Aversion and Reference Dependence Effects on Brand Choice,” Marketing Science 12 (1993): 378–94.

**
The Social Life of Money
** by
Nigel Dodd

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accounting loophole / creative accounting, bank run, banking crisis, banks create money, Bernie Madoff, bitcoin, blockchain, borderless world, Bretton Woods, BRICs, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computer age, conceptual framework, credit crunch, cross-subsidies, David Graeber, debt deflation, dematerialisation, disintermediation, eurozone crisis, fiat currency, financial exclusion, financial innovation, Financial Instability Hypothesis, financial repression, floating exchange rates, Fractional reserve banking, German hyperinflation, Goldman Sachs: Vampire Squid, Hyman Minsky, illegal immigration, informal economy, interest rate swap, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, joint-stock company, Joseph Schumpeter, Kula ring, laissez-faire capitalism, land reform, late capitalism, liberal capitalism, liquidity trap, litecoin, London Interbank Offered Rate, M-Pesa, Marshall McLuhan, means of production, mental accounting, microcredit, mobile money, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, negative equity, new economy, Nixon shock, Occupy movement, offshore financial centre, paradox of thrift, payday loans, Peace of Westphalia, peer-to-peer, peer-to-peer lending, Ponzi scheme, post scarcity, postnationalism / post nation state, predatory finance, price mechanism, price stability, quantitative easing, quantitative trading / quantitative ﬁnance, remote working, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Satoshi Nakamoto, Scientific racism, seigniorage, Skype, Slavoj Žižek, South Sea Bubble, sovereign wealth fund, special drawing rights, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, Veblen good, Wave and Pay, Westphalian system, WikiLeaks, Wolfgang Streeck, yield curve, zero-coupon bond

As if in a higher plane of reality, the symbol seems to operate in an incomprehensible, mystical way, understood and controllable only by the magic of brokers, accountants, lawyers, and financiers … like spellbound savages in the presence of the holy, we watch in wonder the solemn proceedings, feeling in a vague, somewhat fearful way that our lives and the happiness of our children are at the mercy of mysterious forces beyond our control. (Desmonde 1962) Not all work in this area, it must be said, contradicts the classical view of money’s cultural destructiveness. LiPuma argues that the proliferation and ascendance of quantitative finance in contemporary capitalism has transformed social imaginaries (LiPuma 1999), and Poovey suggests that finance is eroding “humanism” by excluding all value that cannot be brought into the calculative apparatus (this idea is very close to Simmel) (Poovey 2001). Maurer, however, sees a deeper “anxiety about number” in the argument that money works like acid, stripping social life away from everything it touches.

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

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

Over the years, and specifically through this book, those traits of his have made us all better off. One more aside before my foreword begins in earnest. I briefly considered not writing this foreword in protest or as a rearguard action. Antti gives away a lot in this book. Perhaps few true secrets (though there are some!), as this is more a textbook than original research. But he makes much of what has become called “quantitative finance” easily (OK, 300+ dense pages, in original format, of “easily”) accessible in one place. Long term, that can’t be good for people like me who make their livings from this stuff being at least a bit secret. But being skilled at game theory I quickly determined that if I didn’t write the foreword somebody else would, so refusal would not accomplish much. With that strategy shot down, I very fleetingly considered having him killed, but this seemed to entail too much “tail risk” (see Chapters 15 and 19) and, anyway, is at least somewhat morally ambiguous.

**
Money Changes Everything: How Finance Made Civilization Possible
** by
William N. Goetzmann

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Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bretton Woods, Brownian motion, capital asset pricing model, Cass Sunstein, collective bargaining, colonial exploitation, compound rate of return, conceptual framework, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, delayed gratification, Detroit bankruptcy, disintermediation, diversified portfolio, double entry bookkeeping, Edmond Halley, en.wikipedia.org, equity premium, financial independence, financial innovation, financial intermediation, fixed income, frictionless, frictionless market, full employment, high net worth, income inequality, index fund, invention of the steam engine, invention of writing, invisible hand, James Watt: steam engine, joint-stock company, joint-stock limited liability company, laissez-faire capitalism, Louis Bachelier, mandelbrot fractal, market bubble, means of production, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, new economy, passive investing, Paul Lévy, Ponzi scheme, price stability, principal–agent problem, profit maximization, profit motive, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, spice trade, stochastic process, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, time value of money, too big to fail, trade liberalization, trade route, transatlantic slave trade, transatlantic slave trade, tulip mania, wage slave

Despite the crashes—or perhaps because of them—financial markets have continually challenged the best and brightest minds with puzzles that hold the promise of intellectual and pecuniary rewards. Perhaps it is the latter that is responsible for the emergence in Europe of a strikingly novel mathematical tradition to understand and place limits on uncertainty about the future. The mathematical roots of modern quantitative finance and complex financial engineering can be traced more specifically to a strong French tradition that audaciously sought to model the investment process and market prices with the tools of probability. From Regnault to Lefèvre to Bachelier to Lévy to Black and Scholes and finally to Mandelbrot, we have seen how the notion of randomness was engineered and then re-engineered to help understand what the invisible hand managed to do without self-awareness.

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Hedge Fund Market Wizards
** by
Jack D. Schwager

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asset-backed security, backtesting, banking crisis, barriers to entry, beat the dealer, Bernie Madoff, Black-Scholes formula, British Empire, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, Edward Thorp, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative ﬁnance, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

*This appendix was originally published in Market Wizards (1989). About the Author Mr. Schwager is a recognized industry expert in futures and hedge funds and the author of a number of widely acclaimed financial books. He is currently the co-portfolio manager for the ADM Investor Services Diversified Strategies Fund, a portfolio of futures and FX managed accounts. He is also an advisor to Marketopper, an India-based quantitative trading firm, supervising a major project that will adapt their trading technology to trade a global futures portfolio. Previously, Mr. Schwager was a partner in the Fortune Group, a London-based hedge fund advisory firm, acquired by the Close Brothers Group. His previous experience also includes 22 years as director of futures research for some of Wall Street’s leading firms and 10 years as the coprincipal of a CTA.

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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, John Meriwether, 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, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, 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, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

These would no longer be allowed under the new rule. Goldman Sachs also had a large proprietary trading desk generating almost 50% of the firm’s profits. For example, in 2007, the trading and principal investments group made 64% of Goldman Sach’s revenues.8 This proprietary trading desk would have to be shut down. In fact, Morgan Stanley has already begun preparing for these new rules and the head of their quantitative trading desk, Peter Muller, and the rest of his team have left Morgan to start their own hedge fund. Some Thoughts The purpose of the Volker rule is to prevent banks that are protected by the public sector safety net from having risks due to investments in hedge funds and/or proprietary trading desks which can increase their risk substantially. For example, take MF Global, which was a successful broker-dealer.