How I Became a Quant: Insights From 25 of Wall Street's Elite
by
Richard R. Lindsey
and
Barry Schachter
Published 30 Jun 2007
I did not know—and still do not know—exactly what they did, but whenever his group was mentioned, people were abuzz with excitement. Peter was outgoing and friendly and made an effort to get to know the quant research guys. We became friends and he was eventually a big influence on my decision to pursue quantitative trading. Quant Research and the Mathematics of Portfolio Trading I want to start with some general comments about how I view research in financial mathematics. I believe that one incredibly important application of quantitative research is in improving business processes. Many business units in finance are governed by rules of thumb and best practices that have never been fully analyzed.
The Quants
by
Scott Patterson
Published 2 Feb 2010
As stocks tumbled, pressure increased on portfolio insurers to sell futures, racing to keep up with the widely gapping market in a devastating feedback loop. The arbs scrambled to put on their trades but were overwhelmed: futures and stocks were falling in unison. Chaos ruled. Fischer Black watched the disaster with fascination from his perch at Goldman Sachs in New York, where he’d taken a job managing quantitative trading strategies. Robert Jones, a Goldman trader, dashed into Black’s office to report on the carnage. “I put in an order to sell at market and it never filled,” he said, describing a frightening scenario in which prices are falling so fast there seems to be no set point where a trade can be executed.
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Then he quit. “Who the fuck are you, and why the fuck do you get an office?” “I’m fucking Peter Muller, and I’m fucking pleased to meet you.” Muller stared bullets at the wiseass Morgan Stanley salesman who’d barged into his office as though he owned it. Muller had only recently begun setting up a quantitative trading group at Morgan, and this was the reception he got? It had been like this since the day he arrived at the bank. After accepting a job at Morgan, and with it an incredible increase in pay, he’d given notice at BARRA and taken six weeks of R&R, spending most of it in Kauai, the lush, westernmost island of Hawaii.
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The party in Paris included festivities at the Louvre and a rehearsal dinner at the Musée d’Orsay. It was good to be Ken Griffin. Perhaps too good. MULLER Just as Griffin was starting up Citadel in Chicago, Peter Muller was hard at work at Morgan Stanley in New York trying to put together his own quantitative trading outfit using the models he’d devised at BARRA. In 1991, he pulled the trigger, flipping on the computers. It was a nightmare. Nothing worked. The sophisticated trading models he’d developed at BARRA were brilliant in theory. But when Muller actually traded with them, he ran into all sorts of problems.
High-Frequency Trading
by
David Easley
,
Marcos López de Prado
and
Maureen O'Hara
Published 28 Sep 2013
David recently co-authored the book Networks, Crowds and Markets: Reasoning About a Highly Connected World, which combines scientific perspectives from economics, computing and information science, sociology and applied mathematics to describe the emerging field of network science. Marcos López de Prado is head of quantitative trading and research at HETCO, the trading arm of Hess Corporation, a Fortune 100 company. Previously, Marcos was head of global quantitative research at Tudor Investment Corporation, where he also led high-frequency futures trading. In addition to more than 15 years of investment management experience, Marcos has received several academic appointments, including postdoctoral research fellow of RCC at Harvard University, visiting scholar at Cornell University, and research affiliate at Lawrence Berkeley National Laboratory (US Department of Energy’s Office of Science).
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Michael Kearns is professor of computer and information science at the University of Pennsylvania, where he holds secondary appointments in the statistics and operations and information management departments of the Wharton School. His research interests include machine learning, algorithmic game theory, quantitative finance and theoretical computer science. Michael also has extensive experience working with quantitative trading and statistical arbitrage groups, including at Lehman Brothers, Bank of America and SAC Capital. David Leinweber was a co-founder of the Center for Innovative Financial Technology at Lawrence Berkeley National Laboratory. Previously, he was visiting fellow at the Hass School of Business and x i i i i i i “Easley” — 2013/10/8 — 11:31 — page xi — #11 i i ABOUT THE AUTHORS at Caltech.
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He has published in the Journal of Finance, Journal of Business and Economic Statistics and Journal of Financial and Quantitative Analysis, among others. He has been a member of the Group of Economic Advisors of the European Securities and Market Authority (ESMA) since 2011. Yuriy Nevmyvaka has extensive experience in quantitative trading and statistical arbitrage, including roles as portfolio manager and head of groups at SAC Capital, Bank of America and Lehman Brothers. He has also published extensively on topics in algorithmic trading and market microstructure, and is a visiting scientist in the computer and information science department at the University of Pennsylvania.
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by
Ernie Chan
Published 17 Nov 2008
xvii P1: JYS fm JWBK321-Chan September 24, 2008 13:43 xviii Printer: Yet to come P1: JYS fm JWBK321-Chan September 24, 2008 13:43 Printer: Yet to come Quantitative Trading xix P1: JYS fm JWBK321-Chan September 24, 2008 13:43 xx Printer: Yet to come P1: JYS c01 JWBK321-Chan September 24, 2008 13:44 Printer: Yet to come CHAPTER 1 The Whats, Whos, and Whys of Quantitative Trading f you are curious enough to pick up this book, you probably have already heard of quantitative trading. But even for readers who learned about this kind of trading from the mainstream media before, it is worth clearing up some common misconceptions. Quantitative trading, also known as algorithmic trading, is the trading of securities based strictly on the buy/sell decisions of computer algorithms.
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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.
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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.
Frequently Asked Questions in Quantitative Finance
by
Paul Wilmott
Published 3 Jan 2007
Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives
by
Satyajit Das
Published 15 Nov 2006
A few firms, like Salomon Brothers, continued to undertake serious research, producing high quality material and introducing sophisticated analytical tools, but they were the exceptions. Serious research moved into the private domain and was mainly applied to prop trading. Salomon’s research effort drove the quantitative trading of its Arbitrage Group and subsequently that of its alumni in Long Term Capital Management (LTCM). But for the most part, the research distributed by investment banks to clients evolved into ‘puffery’ designed to get the client to trade. Analysts were expected to build profile for the firm and for themselves.
Finding Alphas: A Quantitative Approach to Building Trading Strategies
by
Igor Tulchinsky
Published 30 Sep 2019
Griffin and Sunny Mahajan 20 Fundamental Analysis and Alpha Research By Xinye Tang and Kailin Qi 21 Introduction to Momentum Alphas By Zhiyu Ma, Arpit Agarwal, and Laszlo Borda 22 The Impact of News and Social Media on Stock Returns By Wancheng Zhang 23 Stock Returns Information from the Stock Options Market By Swastik Tiwari and Hardik Agarwal 24 Institutional Research 101: Analyst Reports By Benjamin Ee, Hardik Agarwal, Shubham Goyal, Abhishek Panigrahy, and Anant Pushkar 69 77 83 89 95 101 111 121 127 133 135 141 149 155 159 169 179 Contentsix 25 26 27 28 29 30 Event-Driven Investing By Prateek Srivastava Intraday Data in Alpha Research By Dusan Timotity Intraday Trading By Rohit Kumar Jha Finding an Index Alpha By Glenn DeSouza ETFs and Alpha Research By Mark YikChun Chan Finding Alphas on Futures and Forwards By Rohit Agarwal, Rebecca Lehman, and Richard Williams 195 207 217 223 231 241 PART IV NEW HORIZON – WEBSIM 31 Introduction to WebSim By Jeffrey Scott 251 253 PART V A FINAL WORD 32 The Seven Habits of Highly Successful Quants By Richard Hu and Chalee Asavathiratham 263 265 References Index 273 291 Preface Much has changed since we published the first edition of Finding Alphas, in 2015. The premise of that edition – that we considered these techniques “the future of trading” – is more true today than it ever was. In the intervening four years, we at WorldQuant have seen remarkable growth in our development of predictive algorithms for quantitative trading – we call them “alphas” – powered by an ever-rising volume and variety of available data, an explosion in computer hardware and software, and increasingly sophisticated techniques that allow us to create and deploy a higher volume and quality of alphas. Today, at WorldQuant, we have produced over 20 million alphas, a number that continues to grow exponentially as we hunt for ever-weaker predictive signals.
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But change is a constant, and the task is never done. Igor Tulchinsky June 2019 Preface (to the Original Edition) This book is a study of the process of finding alphas. The material is presented as a collection of essays, providing diverse viewpoints from successful quants on the front lines of quantitative trading. A wide variety of topics is covered, ranging from theories about the existence of alphas, to the more concrete and technical aspects of alpha creation. Part I presents a general introduction to alpha creation and is followed by a brief account of the alpha life cycle and insights on cutting losses.
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In Part IV, we introduce you to WebSim, a web-based alpha development tool. We invite all quant enthusiasts to utilize this free tool to learn about alpha backtesting (also known as alpha simulation) and ultimately to create their own alphas. Finally, in Part V, we present an inspirational essay for all quants who are ready to explore the world of quantitative trading. Acknowledgments In these pages, we present a collection of chapters on the algorithmic- based process of developing alphas. The authors of these chapters are WorldQuant’s founder, directors, managers, portfolio managers, and quantitative researchers. This book has two key objectives: to present as many state-of-the-art viewpoints as possible on defining an alpha, and the techniques involved in finding and testing alphas.
Nerds on Wall Street: Math, Machines and Wired Markets
by
David J. Leinweber
Published 31 Dec 2008
This system, which was kept going strong for 12 years in the form of Investment Technology Group (ITG)’s QuantEx, used AI techniques Perils and Pr omise of Evolutionary Computation on Wall Str eet 189 to allow people to build intelligent graph-watching assistants, but it didn’t do any learning. There were all sorts of uses for it, ranging from market surveillance to proprietary trading, but it just did what you told it to do, no matter how stupid that might be. If you had actually found the holy grail of quantitative trading, but had made only one little mistake—you were buying when you should sell, and selling when you should buy—the system would never notice. But a genetic algorithm would notice this kind of sign error, along with a host of other mistakes and miscalibrations. It seemed to be an ideal tool for tuning, refining, and evolving quantitative investment and trading strategies.
My Life as a Quant: Reflections on Physics and Finance
by
Emanuel Derman
Published 1 Jan 2004
Topics in Market Microstructure
by
Ilija I. Zovko
Published 1 Nov 2008
The Misbehavior of Markets: A Fractal View of Financial Turbulence
by
Benoit Mandelbrot
and
Richard L. Hudson
Published 7 Mar 2006
Advances in Financial Machine Learning
by
Marcos Lopez de Prado
Published 2 Feb 2018
Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues
by
Alain Ruttiens
Published 24 Apr 2013
The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street
by
Justin Fox
Published 29 May 2009
The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
by
Gregory Zuckerman
Published 5 Nov 2019
As a result, Morgan Stanley didn’t have strong legal grounds to stop Frey’s trading. With some trepidation, he ignored Morgan Stanley’s continuing threats and began trading. * * * = By 1990, Simons had high hopes Frey and Kepler might find success with their stock trades. He was even more enthused about his own Medallion fund and its quantitative-trading strategies in bond, commodity, and currency markets. Competition was building, however, with some rivals embracing similar trading strategies. Simons’s biggest competition figured to come from David Shaw, another refugee of the Morgan Stanley APT group. After leaving the bank in 1988, the thirty-six-year-old Shaw, who had received his PhD from Stanford University, was courted by Goldman Sachs and was unsure whether to accept the job offer.
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Privately, Englander wondered if Simons’s true fear was the possibility of additional departures from his firm, rather than any theft. Simons wouldn’t share much with his rival. He and Renaissance sued Englander’s firm, as well as Volfbeyn and Belopolsky, while the traders brought countersuits against Renaissance. Amid the hostilities, Volfbeyn and Belopolsky set up their own quantitative-trading system, racking up about $100 million of profits while becoming, as Englander told a colleague, some of the most successful traders Englander had encountered. At Renaissance, Volfbeyn and Belopolsky had signed nondisclosure agreements prohibiting them from using or sharing Medallion’s secrets.
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There’s reason to believe computer traders can amplify or accelerate existing trends. Author and former risk manager Richard Bookstaber has argued that risks today are significant because the embrace of quant models is “system-wide across the investment world,” suggesting that future troubles for these investors would have more impact than in the past.12 As more embrace quantitative trading, the very nature of financial markets could change. New types of errors could be introduced, some of which have yet to be experienced, making them harder to anticipate. Until now, markets have been driven by human behavior, reflecting the dominant roles played by traders and investors. If machine learning and other computer models become the most influential factors in markets, they may become less predictable and maybe even less stable, since human nature is roughly constant while the nature of this kind of computerized trading can change rapidly.
Red-Blooded Risk: The Secret History of Wall Street
by
Aaron Brown
and
Eric Kim
Published 10 Oct 2011
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.
The Volatility Smile
by
Emanuel Derman,Michael B.Miller
Published 6 Sep 2016
A Primer for the Mathematics of Financial Engineering
by
Dan Stefanica
Published 4 Apr 2008
The Physics of Wall Street: A Brief History of Predicting the Unpredictable
by
James Owen Weatherall
Published 2 Jan 2013
Python for Algorithmic Trading: From Idea to Cloud Deployment
by
Yves Hilpisch
Published 8 Dec 2020
Fluent Python: Clear, Concise, and Effective Programming. 2nd ed. Sebastopol: O’Reilly. VanderPlas, Jake. 2016. Python Data Science Handbook: Essential Tools for Working with Data. Sebastopol: O’Reilly. Background information about algorithmic trading can be found, for instance, in these books: Chan, Ernest. 2009. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. Hoboken et al: John Wiley & Sons. Chan, Ernest. 2013. Algorithmic Trading: Winning Strategies and Their Rationale. Hoboken et al: John Wiley & Sons. Kissel, Robert. 2013. The Science of Algorithmic Trading and Portfolio Management.
Market Risk Analysis, Quantitative Methods in Finance
by
Carol Alexander
Published 2 Jan 2007
Python for Finance
by
Yuxing Yan
Published 24 Apr 2014
High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
by
Irene Aldridge
Published 1 Dec 2009
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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As with technical trading, fundamental trading entails buying a security the price of which was deemed too low relative to its analytically determined fundamental value and selling a security the price of which is considered too high. Like technical trading, fundamental trading can also be applied at any frequency, although price formation or microstructure effects may result in price anomalies at ultrahigh frequencies. Finally, quant (short for quantitative) trading refers to making portfolio allocation decisions based on scientific principles. These principles may be fundamental or technical or can be based on simple statistical relationships. The main difference between quant analyses and technical or fundamental styles is that quants use little or no discretionary judgments, whereas fundamental analysts may exercise discretion in rating the management of the company, for example, and technical analysts may “see” various shapes appearing in the charts.
Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis
by
Scott Patterson
Published 5 Jun 2023
The exact timing of such interventions is typically unforeseen by economic actors like banks and corporations, and the fallout from them can be chaotic. So while the models were able to make precise forecasts based on past events, the chaotic shocks often made those precise forecasts practically worthless. In 1986, Litterman was contacted by Goldman Sachs. The firm had been dabbling in quantitative trading strategies. Its most prominent hire was the economist Fischer Black, co-creator of the Black-Scholes option pricing model and a staunch, practically religious believer in the efficient market hypothesis. In 1986, Litterman sat down for an interview with Black. “So Bob, you’re an econometrician,” he said.
More Money Than God: Hedge Funds and the Making of a New Elite
by
Sebastian Mallaby
Published 9 Jun 2010
Much like the Simons team, he pursued numerical precision with a zealous intensity: His staff soon discovered that it was no good telling him that a programming task might take three to eight weeks; you had to say that it would take 5.25, but with an error of two weeks.7 Yet for all these similarities, there were differences between Shaw and Simons too. These proved to be significant. Shaw got into finance via Morgan Stanley’s proprietary trading desk, which hired him to create a computer system to support its quantitative trading. It was 1986, and big things were stirring at Morgan. The firm’s secretive Analytical Proprietary Trading unit ran a computerized effort to profit from short-run liquidity effects in stock markets. As Michael Steinhardt had discovered in the 1970s, a big sell order from a pension fund could push a stock’s price out of line; provided that there was no information behind the sale—that is, provided that the pension fund was selling because it needed cash rather than because it was reacting to bad news—Steinhardt could profit by buying and holding the stock until it rose back to its previous level.
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Medallion therefore closed to new outside investors in 1993, and by the 2000s the $6 billion or so in the fund consisted almost entirely of employees’ money.34 But the very existence of Medallion had a halo effect on the rest of the industry, offsetting the blow to the reputation of black-box trading administered by the collapse of Long-Term Capital. Each time Simons’s picture appeared on the cover of a financial magazine, more eager institutional money flooded into quantitative trading systems. Simons himself capitalized on this phenomenon. In 2005 he launched a new venture, the Renaissance Institutional Equities Fund, which was designed to absorb an eye-popping $100 billion in institutional savings. The only way this huge amount could be manageable was to branch out from short-term trading into more liquid longer-term strategies—and since pure pattern recognition works best for short-term trades, it followed that Simons was offering a fund that would rely on different sorts of signal—ones that might already have been mined by D.
Extreme Money: Masters of the Universe and the Cult of Risk
by
Satyajit Das
Published 14 Oct 2011
Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
by
Sebastien Page
Published 4 Nov 2020
Why Stock Markets Crash: Critical Events in Complex Financial Systems
by
Didier Sornette
Published 18 Nov 2002
Adaptive Markets: Financial Evolution at the Speed of Thought
by
Andrew W. Lo
Published 3 Apr 2017
In evolutionary terms, the markets were adapting. In fact, the markets may have been adapting specifically to the presence of D. E. Shaw & Co., although Shaw modestly downplays that possibility: “Over time, things evolved. I don’t know how much of that was due to our influence. The general comment I can make is that quantitative trading became more challenging with every passing year.” In fact, Shaw inspired legions of talented computer scientists, mathematicians, and other quants to pursue careers in finance, raising the level of play in this highly competitive field. After transforming the hedge fund industry into a quantitative discipline that now employs thousands of engineers, Shaw decided to apply his intellectual gifts to another field.
The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton
by
Colin Read
Published 16 Jul 2012
Safe Haven: Investing for Financial Storms
by
Mark Spitznagel
Published 9 Aug 2021
Makers and Takers: The Rise of Finance and the Fall of American Business
by
Rana Foroohar
Published 16 May 2016
A Man for All Markets
by
Edward O. Thorp
Published 15 Nov 2016
The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds
by
Maneet Ahuja
,
Myron Scholes
and
Mohamed El-Erian
Published 29 May 2012
More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded)
by
Michael J. Mauboussin
Published 1 Jan 2006
R Cookbook
by
Paul Teetor
Published 28 Mar 2011
Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
by
William Poundstone
Published 18 Sep 2006
Antifragile: Things That Gain From Disorder
by
Nassim Nicholas Taleb
Published 27 Nov 2012
The Concepts and Practice of Mathematical Finance
by
Mark S. Joshi
Published 24 Dec 2003
No One Would Listen: A True Financial Thriller
by
Harry Markopolos
Published 1 Mar 2010
Mathematical Finance: Theory, Modeling, Implementation
by
Christian Fries
Published 9 Sep 2007
How Markets Fail: The Logic of Economic Calamities
by
John Cassidy
Published 10 Nov 2009
The Little Book of Hedge Funds
by
Anthony Scaramucci
Published 30 Apr 2012
The Scandal of Money
by
George Gilder
Published 23 Feb 2016
Profiting Without Producing: How Finance Exploits Us All
by
Costas Lapavitsas
Published 14 Aug 2013
In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by
Andrew W. Lo
and
Stephen R. Foerster
Published 16 Aug 2021
Models. Behaving. Badly.: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life
by
Emanuel Derman
Published 13 Oct 2011
Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures
by
Frank J. Fabozzi
Published 25 Feb 2008
Statistical Arbitrage: Algorithmic Trading Insights and Techniques
by
Andrew Pole
Published 14 Sep 2007
Competing on Analytics: The New Science of Winning
by
Thomas H. Davenport
and
Jeanne G. Harris
Published 6 Mar 2007
Walled Culture: How Big Content Uses Technology and the Law to Lock Down Culture and Keep Creators Poor
by
Glyn Moody
Published 26 Sep 2022
Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
by
Joel Hasbrouck
Published 4 Jan 2007
Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better
by
Andrew Palmer
Published 13 Apr 2015
A First-Class Catastrophe: The Road to Black Monday, the Worst Day in Wall Street History
by
Diana B. Henriques
Published 18 Sep 2017
On Wednesday, September 10, the SEC was scheduled to hold a public meeting to vote on a staff plan for calming down witching hour Fridays. The proposal asked that brokers send orders seeking the day’s closing price, so-called market-on-close orders, to the trading floor a half hour before the closing bell, so any order imbalances could be publicized. That was as far as John Shad was willing to go in meddling with the new quantitative trading strategies. Before that public session, however, Shad convened a “super-executive session,” a meeting open only to the commissioners and a few key members of the enforcement staff. According to one account, this meeting was the first update on the Boesky case given to Shad or any other commissioner since earlier that summer.
The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling
by
Adam Kucharski
Published 23 Feb 2016
The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction
by
Richard Bookstaber
Published 1 May 2017
Bad Data Handbook
by
Q. Ethan McCallum
Published 14 Nov 2012
The End of Accounting and the Path Forward for Investors and Managers (Wiley Finance)
by
Feng Gu
Published 26 Jun 2016
Mathematics for Finance: An Introduction to Financial Engineering
by
Marek Capinski
and
Tomasz Zastawniak
Published 6 Jul 2003
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
by
Nassim Nicholas Taleb
Published 1 Jan 2001
Five Billion Years of Solitude: The Search for Life Among the Stars
by
Lee Billings
Published 2 Oct 2013
Boom and Bust: A Global History of Financial Bubbles
by
William Quinn
and
John D. Turner
Published 5 Aug 2020
Value of Everything: An Antidote to Chaos The
by
Mariana Mazzucato
Published 25 Apr 2018
Other People's Money: Masters of the Universe or Servants of the People?
by
John Kay
Published 2 Sep 2015
The Missing Billionaires: A Guide to Better Financial Decisions
by
Victor Haghani
and
James White
Published 27 Aug 2023
Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by
Greg N. Gregoriou
,
Vassilios Karavas
,
François-Serge Lhabitant
and
Fabrice Douglas Rouah
Published 23 Sep 2004
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
by
Wes McKinney
Published 25 Sep 2017
Crisis Economics: A Crash Course in the Future of Finance
by
Nouriel Roubini
and
Stephen Mihm
Published 10 May 2010
Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least
by
Antti Ilmanen
Published 24 Feb 2022
The Tyranny of Nostalgia: Half a Century of British Economic Decline
by
Russell Jones
Published 15 Jan 2023
The Default Line: The Inside Story of People, Banks and Entire Nations on the Edge
by
Faisal Islam
Published 28 Aug 2013
Radical Uncertainty: Decision-Making for an Unknowable Future
by
Mervyn King
and
John Kay
Published 5 Mar 2020
All the Devils Are Here
by
Bethany McLean
Published 19 Oct 2010
Thinking, Fast and Slow
by
Daniel Kahneman
Published 24 Oct 2011
The Social Life of Money
by
Nigel Dodd
Published 14 May 2014
Money Changes Everything: How Finance Made Civilization Possible
by
William N. Goetzmann
Published 11 Apr 2016
Expected Returns: An Investor's Guide to Harvesting Market Rewards
by
Antti Ilmanen
Published 4 Apr 2011
Corporate Finance: Theory and Practice
by
Pierre Vernimmen
,
Pascal Quiry
,
Maurizio Dallocchio
,
Yann le Fur
and
Antonio Salvi
Published 16 Oct 2017
Principles of Corporate Finance
by
Richard A. Brealey
,
Stewart C. Myers
and
Franklin Allen
Published 15 Feb 2014
Learn Algorithmic Trading
by
Sebastien Donadio
Published 7 Nov 2019
Contributors About the authors Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Société Générale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University.
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Apart from his role at RxDataScience, and is also currently affiliated with Imperial College, London. Ratanlal Mahanta is currently working as a quantitative analyst at bittQsrv, a global quantitative research company offering quant models for its investors. He has several years of experience in the modeling and simulation of quantitative trading. Ratanlal holds a master's degree in science in computational finance, and his research areas include quant trading, optimal execution, and high-frequency trading. He has over 9 years' work experience in the finance industry, and is gifted at solving difficult problems that lie at the intersection of the market, technology, research, and design.
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You will be introduced to algorithmic trading and setting up the environment required to perform tasks throughout the book. You will learn the key components of an algorithmic trading business and the questions you need to ask before embarking on an automated trading project. Later, you will learn how quantitative trading signals and trading strategies are developed. You will get to grips with the workings and implementation of some well-known trading strategies. You will also understand, implement, and analyze more sophisticated trading strategies, including volatility strategies, economic release strategies, and statistical arbitrage.
Electronic and Algorithmic Trading Technology: The Complete Guide
by
Kendall Kim
Published 31 May 2007
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.
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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.
The Ethical Algorithm: The Science of Socially Aware Algorithm Design
by
Michael Kearns
and
Aaron Roth
Published 3 Oct 2019
We’ve spent many hours talking to lawyers, regulators, economists, criminologists, social scientists, technology industry professionals, and many others about the issues raised in these pages. We’ve provided testimony and input to congressional committees, corporations, and government agencies on algorithmic privacy and fairness. And between us we have extensive, hands-on professional experience in areas including quantitative trading and finance; legal, regulatory, and algorithmic consulting; and technology investing and start-ups—all of which are beginning to confront the social issues that are our themes here. We are, in short, modern computer scientists. We also know what we are not, and should not pretend to be. We are not lawyers or regulators.
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If we then change the weightings—say, to 1/4 times error plus 3/4 times the unfairness score—we will find another point on the Pareto frontier. So by exploring different combinations of our two objectives, we “reduce” our problem to the single-objective case and can trace out the entire frontier. While the idea of considering cold, quantitative trade-offs between accuracy and fairness might make you uncomfortable, the point is that there is simply no escaping the Pareto frontier. Machine learning engineers and policymakers alike can be ignorant of it or refuse to look at it. But once we pick a decision-making model (which might in fact be a human decision-maker), there are only two possibilities.
Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino
by
Jim McTague
Published 1 Mar 2011
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.
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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.
Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
by
Lasse Heje Pedersen
Published 12 Apr 2015
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.
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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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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.
Doing Good Better: How Effective Altruism Can Help You Make a Difference
by
William MacAskill
Published 27 Jul 2015
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.
Trend Commandments: Trading for Exceptional Returns
by
Michael W. Covel
Published 14 Jun 2011
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.
Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
by
David Aronson
Published 1 Nov 2006
Jensen, R.R. Johnson, and J.M. Mercer, “Tactical Asset Allocation and Commodity Futures,” Journal of Portfolio Management 28, no. 4 (Summer 2002). 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.
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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. CHAPTER 8 Case Study of Rule Data Mining for the S&P 500 1. J.P. Romano and M. Wolf, “Stepwise Multiple Testing as Formalized Data Snooping,” Econometrica 73, no. 4 (July 2005), 1237–1282. 508 NOTES 2.
Breaking Twitter: Elon Musk and the Most Controversial Corporate Takeover in History
by
Ben Mezrich
Published 6 Nov 2023
Elon was no stranger to the mythology that had grown up around the kid: Sam Bankman-Fried, or SBF as he was generally known in the tech press, was considered one of the most brilliant young entrepreneurs of the past decade. After first making a name for himself at Jane Street Capital, then starting his own quantitative trading firm, called Alameda Research, at thirty, SBF had founded FTX, one of the fastest-growing crypto exchanges in the world. The stories about SBF were legendary, and unavoidable. How he’d first pitched FTX to a room full of Sequoia Capital VCs, waxing poetic about making his exchange “a place where you can do anything you want with your next dollar,” from buying crypto to art to goddamn produce, all the while his head hovered inches above his laptop, never once breaking from the screen to make eye contact.
Market Sense and Nonsense
by
Jack D. Schwager
Published 5 Oct 2012
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
Published 1 Nov 2009
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.
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by
Thomas H. Davenport
and
Julia Kirby
Published 23 May 2016
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.
Value Investing: From Graham to Buffett and Beyond
by
Bruce C. N. Greenwald
,
Judd Kahn
,
Paul D. Sonkin
and
Michael van Biema
Published 26 Jan 2004
And he followed Graham's practice of comparing two companies in the same industry, like Bethlehem and Crucible Steel, to see which was cheaper on an intrinsic value basis. Their focus was the balance sheet, not the income statement. They were able to discuss these ideas and pick up other suggestions from a community of value investors that had formed around Graham. One lasting interest of Graham and this circle was the search for quantitative trading formulas that could be used to direct market investment strategies in a disciplined way. Heilbrunn contributed to the development of these kinds of rules in an article published in 1958. The method prefigured many of the formulas used by quantitatively oriented value investors today. Heilbrunn examined the price, earnings, and dividend histories of specific companies to establish the ranges of the price to earnings (P/E) multiple and the dividend yield within which the securities had traded.
You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All
by
Adrian Hon
Published 14 Sep 2022
Users gain karma on Reddit by making posts and comments that are upvoted by other users, and lose karma through downvotes. It’s not hard to get one thousand karma; my very sporadic participation over the last decade has gained me five thousand karma, which is tiny compared with more active users. Aside from qualifying you to apply to weird quantitative trading jobs, Reddit karma has basically no use whatsoever, except in one very limited and specific situation: the display order of posts and comments. Most Reddit communities show highly upvoted contributions at the top of the page, meaning they attract more attention and replies. If you’re adept at posting content that gets a reaction, whether that’s smart insights, memes, or offensive jokes, you can command the attention of millions.
Fortunes of Change: The Rise of the Liberal Rich and the Remaking of America
by
David Callahan
Published 9 Aug 2010
Shaw Group, a large hedge fund that had $39 billion in assets before the crash and offices on three continents. Shaw started out as an academic, getting his PhD from Stanford University in 1980 and then joining the faculty of Columbia University’s computer science department, where he led work on supercomputers. Shaw later moved to the investment world, where he made a killing using advanced quantitative trading methodologies—an approach that led Fortune to call him “King Quant.” Eventually, Shaw drifted back to computer science, founded a research firm, and affiliated again with Columbia. But he remains involved with his hedge fund—involved enough to make $275 million for himself in 2008—and avidly pursues his passion of liberal politics.
A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation
by
Richard Bookstaber
Published 5 Apr 2007
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.
The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by
Kariappa Bheemaiah
Published 26 Feb 2017
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. 30Jacky 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.
Transaction Man: The Rise of the Deal and the Decline of the American Dream
by
Nicholas Lemann
Published 9 Sep 2019
More and more lines of business involved technical, quantitative computer-driven financial transactions, running twenty-four hours a day, designed by people with advanced academic training whom the firm had hired. Dick Fisher, and John Mack after him, liked to say that three forces were driving the new financial world: deregulation, globalization, and technology. Morgan Stanley pursued enthusiastically anything that seemed attuned to those forces. The firm started a quantitative trading division run by a team of mathematicians. It invented new kinds of derivatives. It became a force in Silicon Valley, where its star analyst, Mary Meeker, wrote optimistic reports about the future of technology and also made Morgan Stanley the leading manager of the initial public offerings of companies like Netscape and Google.
Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness
by
Frederic Laloux
and
Ken Wilber
Published 9 Feb 2014
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).
Making Globalization Work
by
Joseph E. Stiglitz
Published 16 Sep 2006
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 long-lasting adverse effects.).NAFTA proponents sometimes argue that NAFTA’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.
The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power
by
Max Chafkin
Published 14 Sep 2021
Macro investors might only try to make a trade a week—an approach that fit perfectly with Thiel’s unusual combination of indecisiveness and high tolerance for risk. “His worldview is that if you get one big thing right, and move hard with conviction, then nothing else matters,” said an early Clarium employee. It also fit with his appetite for power: Quantitative trading’s effects on the market are in most cases hard to parse; macro investors, because their bets tend to be large, involving entire economies, can move—and in rare cases, destroy—those economies. If you bet enough money on a recession as a macro investor, you may end up causing one. Thiel’s choice of strategy was contrarian in another way.
The Extended Phenotype: The Long Reach of the Gene
by
Richard Dawkins
Published 1 Jan 1982
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.
The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated
by
Gautam Baid
Published 1 Jun 2020
—Morgan Housel Recency bias is all-pervasive. We tend to extrapolate recent trends into infinity as we assume them to reflect the new normal. Until it isn’t normal in a cyclical world (figures 25.1a and 25.1b). FIGURE 25.1 (a) Abrupt volatility is the norm in life and (b) financial markets. Source: Behavior Gap. If a quantitative trading strategy works consistently for an initial period, it tends to become widespread. In 1998, it was convergence arbitrage. LTCM assumed that historical patterns in the relationship of certain assets would persist forever and leveraged its positions by more than 25 to 1. When those patterns changed for just a brief period, LTCM blew up.
Alpha Trader
by
Brent Donnelly
Published 11 May 2021
Incorporate overbought and oversold indicators into your process and use them to fine tune entries and exits, but don’t be the trader who fades a move solely because “It’s come too far, too fast.” That is not a valid trading strategy. Remember we are using these technicals as guidance and tactical inputs and not as a systematic quantitative trading framework. MORNING QUICK TECHS Most trading jobs are busiest in the morning. Markets are already moving when you sit down but still, you need to prepare for the day. By 7 a.m. each day, I am fielding client queries, producing content, planning my day and going through 100s of e-mails. I need to keep my morning process as simple as possible.
Hedge Fund Market Wizards
by
Jack D. Schwager
Published 24 Apr 2012
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.
The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal
by
Ludwig B. Chincarini
Published 29 Jul 2012
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.