Table of Contents Cover Foreword Preface CHAPTER 1: Quake CHAPTER 2: The UnRule that Rules the Rest CHAPTER 3: Parallel Universes CHAPTER 4: Signal and Noise CHAPTER 5: Waves CHAPTER 6: Correlation CHAPTER 7: Scaling Up CHAPTER 8: An Exponential World CHAPTER 9: Quant Biology CHAPTER 10: The Age of Prediction Index End User License Agreement The UnRules Man, Machines and the Quest to Master Markets IGOR TULCHINSKY This edition first published 2018 © 2018 Igor Tulchinsky Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher Wiley publishes in a variety of print and electronic formats and by print on demand Some material included with standard print versions of this book may not be included in e books or in print on demand If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com For more information about Wiley products, visit www.wiley.com Designations used by companies to distinguish their products are often claimed as trademarks All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners The publisher is not associated with any product or vendor mentioned in this book Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom If professional advice or other expert assistance is required, the services of a competent professional should be sought Library of Congress Cataloging in Publication Data Names: Tulchinsky, Igor, 1966– author Title: The unrules : man, machines and the quest to master markets / by Igor Tulchinsky Description: Chichester, West Sussex, United Kingdom : John Wiley & Sons, 2018 | Includes index | Identifiers: LCCN 2018003403 (print) | LCCN 2018005290 (ebook) | ISBN 9781119372110 (pdf) | ISBN 9781119372127 (epub) | ISBN 9781119372103 (cloth) Subjects: LCSH: Success in business | Strategic planning | Information technology | Information society | Tulchinsky, Igor, 1966– Classification: LCC HF5386 (ebook) | LCC HF5386 T82865 2018 (print) | DDC 650.1—dc23 LC record available at https://lccn.loc.gov/2018003403 Cover Design: Ed Johnson Foreword Igor Tulchinsky and I had very different formative experiences His childhood was constrained by the spiritual oppression of life in the Soviet Union, while mine was enriched by the opportunities available to middle class kids in 1950s' America Yet we had much in common: caring parents, a love of reading, and a fascination with math As one of today's leading quantitative investors, Igor understands better than most the numbers that underlie dynamic markets “Markets can be seen as waves,” he writes “They resemble the regular oscillations of a musical instrument.” That's a valid observation, although different from the way I came to learn about business and finance As a college student, I was influenced by the writings of the late Nobel Laureate Gary Becker, and by personal experiences that made me realize how many aspiring entrepreneurs – especially minorities and women – were being denied access to capital Igor's approach has relied on rigorous and sophisticated mathematical analysis to identify trading opportunities This might seem very different from a reliance on theories of human capital – the talent, training, and experiences of people – and the effects of societal trends on business success that I use But in reality, we both seek to predict the most likely future based on what we observe Understanding numbers and understanding people can both yield important insights that contribute to financial success And we concur on several important points that are discussed in this book: All markets contain risk, and without risk there are no gains Careful research can discover the price of risk more accurately Markets also contain psychological traps, such as confusing correlation with causation If most people are ensnared by these traps, an objective investor who follows the research – like the proverbial one eyed man in the land of the blind – has an advantage The best investors seek and distill advice from widely diverse sources The study of markets and the study of biology have much in common Each is a data driven information science; each uses predictive algorithms in seeking a needle in a haystack of data As Igor points out, the next great disease breakthrough might be discovered using the same mathematical techniques he uses to analyze financial data Talent is distributed around the world Genius lives everywhere Igor and I both also believe in history's important lessons A 2010 book about financial markets said that “real estate prices collapsed, credit dried up and house building stopped.” That sounds like a description of 2008 But it actually refers to 1792, during the administration of George Washington More recently, stock markets dropped sharply, banks curtailed lending, and unemployment rose to double digits Again, that wasn't 2008, it was 1974 Live long enough and you begin to appreciate what remains constant through cycles of history Yet also note that history isn't a sine wave that repeats patterns exactly; it's more like a helix – similar events return in a different orbit This is why research is crucial Investors who conduct careful research are usually better insulated against inevitable market downturns They understand that the value of debt securities underpins all capital markets, that leverage is a dangerous tool in volatile markets, that ratings are not always a reliable measure of credit quality, that interest rates are not predictable, and that government actions often distort markets Although these basic investing principles change little over time, the tools of finance have changed dramatically When I studied quantitative economics at Berkeley in the 1960s, computers were expensive, relatively inaccessible, room sized machines with little power to model investment scenarios By 1976 processing was speedier, but the storage cost for the IBM System/370 that my business installed was still $1 million per megabyte Today data processing is millions of times faster, available to nearly anyone on earth, with virtually infinite storage in the cloud at a cost that approaches zero This technology revolution has changed the world in many fields Its impact on biomedical research and precision medicine, for example, has accelerated clinical science and saved untold numbers of lives There is great opportunity for it to advance beyond its current state through partnerships such as the WorldQuant Initiative for Quantitative Prediction at Weill Cornell, which Igor founded In the area of finance and investing, Igor and his colleagues now can what 1960s' finance students could only dream of – simulate reality by creating millions of algorithms (called alphas) that identify trading opportunities with remarkable speed and accuracy Although we see markets through different lenses, Igor and I are in complete agreement on one of the most important social issues of our time: providing a path to a meaningful life for every worker, no matter how much traditional work is disrupted by advancing technology In 2017 we co authored a Wall Street Journal opinion article about the challenges of automation and artificial intelligence We concluded that digital innovation and robots are opening new possibilities for workers and that the future workplace can provide the opportunity for lives of purpose We believe, in short, that technology leverages human capital and that wisely deployed technology creates more jobs than it destroys The key, of course, is to provide abundant opportunities for training and retraining The workplace of the future can already be seen in the international operations of Igor's company, WorldQuant Separately, the WorldQuant Foundation's WorldQuant University offers students a tuition free online master's degree program in financial engineering By providing opportunities for a diverse group of bright people who are willing to work hard toward a clear goal, Igor is expanding human capital and helping assure a more prosperous tomorrow The UnRules is a valuable guide for getting there Michael Milken Chairman of the Milken Institute Preface People who know me well are aware that I'm a man of few words In fact, I joke that you only have so many words in life, and when you use them up, you die Of course, now that I've written this book, I'm living dangerously When we are born, our languages are bestowed upon us I was born in the Soviet Union, in Minsk, now the capital of Belarus, and I grew up speaking Russian When my parents and I left the Soviet Union and came to the United States, in the late 1970s, we had to master English As a child, I grasped the new language more easily than my parents did, but – as with the challenging task of adjusting to a strange new culture – we coped Mathematics was a language I felt comfortable with I had played chess as a child, and my parents were professional musicians; both pastimes are rooted in a mathematical, rules based order Soviet schools excelled at teaching math, and when I was in middle school in Wichita, Kansas, I discovered computer programming From the start I was drawn to the precision of early computer languages: BASIC and, later, C When I stumbled into video game development at age 17, I was assigned to co write a book about video game programming My experience in early video gaming – coming up with characters (and jokes), writing the programs, working on the book – convinced me that just about anything is possible This book, The UnRules, is about languages of many kinds: scientific, mathematical, computer, financial, biological It's about codes, patterns, and signals, and the attempt to extract order from a noisy world The notion of the UnRule, which lies at the heart of this book, is a kind of philosophy, based on empirical experiences both in financial markets and in life, where no rule, dogma, ideology, paradigm, or model lasts forever and no trading or market relationship performs as you expect all the time Like a tether on a balloon, the UnRule limits the reach of all the other rules I've gathered over the years For me, an intense involvement in competitive markets, and in building my career and my quantitative investment firm, WorldQuant, led me to develop rules that apply not just to trading but to life Many of those rules are rooted in an always uncertain future This is reflected in my firm's deep involvement in developing alphas – that is, algorithms that seek to predict certain market relationships The alphas we develop, now numbering in the millions, consist of mathematical expressions and computer code We rigorously back test them with historical market data to “simulate” their performance, just as video games simulate different realities Much of this investment process is extensively automated And yet we not just hand over trading to machines People matter Over the years we have learned a lot about alpha design and development We've learned that no matter how well an alpha is back tested, it will probably not perform as well when we put it into real markets, and like the rules, no alpha lasts forever We've learned about the use and dangers of correlation, the management of risk, and the deployment of extraordinary numbers of alphas We've developed a sense of when to assume risk and, very importantly, when to take losses Along these lines, I have found that some life decisions have no clear solutions For many years I made disciplined but incremental empirical decisions – hiring, for instance, only when I could find genuine talent There was no master plan Eventually, we discovered we could find the brightest people in quantitative fields and teach them finance Smart, motivated people learn quickly That search for talent transformed WorldQuant into a global firm, exploiting the fact that talent is universal but opportunities are not My parents and I had to risk a long journey to America to find the freedom to take advantage of opportunities Today WorldQuant offers citizens of many nations those same chances, while allowing them to remain at home – in Bulgaria, China, India, Israel, Russia, and Vietnam, among other countries That recognition that talent requires opportunity also lies behind my recent philanthropic efforts to provide free online education in quantitative disciplines through WorldQuant University, a not for profit entity legally separate from the firm Today we find ourselves in exciting scientific and technological waters The drive of any investment firm is to try to predict the path of a market's complex turbulence, which we have labored to decipher and define through alphas But prediction is never easy There is an unresolved tension captured by the UnRule We have been riding great leaps in computer power and an explosion of data of all kinds We have only just begun to explore this new world, which has amazing possibilities and profound challenges The UnRules ends with that curve of exponential growth in alphas bending toward the sky In WorldQuant we have built a company uniquely suited to this dawning age of broad exponential growth The UnRules is not a long book, but I hope it conveys a sense of the ceaseless searching and testing and experimentation that occur at a firm like WorldQuant In fact, this book is about beginnings rather than endings I'm still not a believer in using too many words, but there will be more to say as we explore this new world in more profound ways Many books have deep roots The UnRules goes back to my childhood, listening to my parents practice their music every day in our apartment in Minsk Authors often thank their parents; none of us would be here without them But mine embodied many of the virtues that found their way into my rules: hard work, persistence, discipline, goal setting, the willingness to take a risk to reach a valuable end, all bound together by love And without Millennium Management's Izzy Englander, WorldQuant would not exist He has been my boss, my mentor, and my friend for many years Parts of this book were first composed in an internal publication for the WorldQuant community in 2013 Wendy Goldman Rohm, my literary agent, was instrumental in conceptualizing aspects of the book and finding a publisher Weill Cornell Medicine's Dr Christopher Mason, the subject of Chapter 9, has entertained and enlightened me in conversation for a number of years, and kindly made sure I got my biology right Several WorldQuant colleagues read parts or all of this book in draft, offering comments and suggestions, pointing out errors, refreshing memories They include Scott Bender, Jeffrey Blomberg, Anuraag Gutgutia, Richard Hu, Geoffrey Lauprete, Nitish Maini, and Paradorn Pasuthip And ably overseeing and managing the editorial process was WorldQuant's global head of content, Michael Peltz Finally, I'd like to acknowledge all my many colleagues at WorldQuant over the years This book, and our success, would not be possible without your faith and support Igor Tulchinsky December 2017 lessons learned xii, 4–5, 9, 17–19, 60–1, 65, 83, 87, 88 leverage viii, liar paradox 18–19 life decisions xii, xiii, 9, 13–17, 18, 61 life is unpredictable rule 17, 21, 86 life cycle aspects of alphas xii, 99, 112, 129–30 Litterman, Bob Lo, Andrew Logo 25–6 log periodic oscillations 57–8 long positions, pairs trading strategies 60–1, 84–6 Long Term Capital Management Lorange, Peter 68–9, 71, 123 Los Alamos, New Mexico 28–30, 72, 91 losses xii, 1, 3, 4–5, 17–18, 19, 59–60, 67 cutting losses 4–5, 17–18, 19, 59–60, 67 luck 67 M6A markers 117–18 McChrystal, Stanley 107 machine learning 103, 111, 132 Malkiel, Burton 40 manage losses 1, 3, 4–5, 17–18, 19, 59–60, 67 managerial skills 88 Mandelbrot, Benoit 91–3, 128 Manhattan, author biography 23–4, 48, 87–8 Manhattan Project's nuclear bomb development 28–31, 44, 72–3 MANIAC computer 31, 74 margin calls market neutralization strategies 85–6 markets, historical background 89–90 Mason, Dr Christopher xiv, 111–12, 116–23, 128, 130, 132, 135 mathematics vii, xi, xii, 3, 9, 16–19, 24–8, 31, 35, 41–3, 66–8, 72–5, 81, 91–2, 101–3, 133– music 16 Soviet Union schools 24 see also alphas Mauchly, John W 30, 44 mean reversion 84 medicine 111–23, 130–2 memories, financial markets 93 Mendel, Gregor 113–14 merger arbitrage 59, 84 Merton, Robert 42, 46 MetaSUB 118–19 methyl group, RNA 117 Metropolis, Nicholas 73, 74 microbes 113–14, 115, 118–21, 129 Milken, Michael ix, 37 Millennium Management xiii, 39, 47, 58–60, 81–8, 97, 106, 129, 133 WorldQuant spin off 97, 101–2 Miller, Jeff 86–7 Minsk xi, xiii, 10, 13, 15, 16, 24 Mississippi, author biography 32 MIT 6, 42, 46, 68, 87 mitochondria 119 mobile phones 104, 130–1 Moivre, Abraham de 75 molecules 41, 56, 89, 114–22, 127–9 momentum price signals 83–4 Monte Carlo simulation 73–6 MOOCs 102–3 Moody's 43 Moore's law 36, 106, 115 moral hazard 71–2 Morgan Stanley 60 Morgenstern, Oskar 42 mortgage backed securities 4, 70–1, 84, 135 multiviewpoint perspectives 108, 134–5 music vii, xiii, 16, 23, 24, 81 financial markets vii, xiii mathematics 16 Musk, Elon 103 mutual funds 40 natural selection 89, 116 Nebuchadnezzar 65 networks data 130–3 power laws 91 see also internet neutralization strategies 85–6 neutrons 73, 128 New York author biography 23–4, 48, 97–8, 106 subway pathogens 118 Nigeria 105 Nobel Prizes 15, 35, 40, 46, 114, 116 noise xi, xii, 9–10, 17–19, 33–5, 41, 46–8, 51–8, 67, 75, 89–93, 99, 127–8 background 33, 35, 46–8, 51, 56, 67, 92–3, 127–8 information contrasts 47 order xi, xii, 19, 58, 75, 89, 92–3, 127–8, 134, 135–6 normal accidents 69–70 normal distributions 75, 93 the North Sea, waves 52–3, 67–70, 123 Norway, shipping companies 68–9, 71, 123 NP complete algorithms 35–6 nuclear bomb development 28–31, 42, 72–3 obsolescence factors, alphas xii, 112 obstacles are information rule 18, 61, 83 oil resources 35, 42, 52–3 one dimensional nonlinear Schrödinger equation (soliton waves) 55–6 Oppenheimer, J Robert 31, 44 opportunities vii, viii, ix, xii, xiii, 6–9, 13–18, 26, 60, 86–7, 102–6, 132–3, 135 options 39, 42, 46, 74 order, noise xi, xii, 19, 58, 75, 89, 92–3, 127–8, 134, 135–6 ordinary least squares regression analysis 44–5 organizational structure, WorldQuant 88–9, 107 overview of the book xi, xii, xiii, Oxford English Dictionary 81 pairs trading strategies 59, 60–1, 82, 84–6 parallel universes 21–32 Pareto, Vilfredo 91 particles 51–2 Pascal, Blaise 29 passive funds 40, 45 past results, future results 72 PathoMap 118–19 patterns xi, 17, 27–8, 47–8, 57–8, 72, 90, 93, 112–13, 123 see also signals Peregrine breathers 55–6 performance evaluations, alphas 66–7, 100 Perrow, Charles 69 persistence xiii, 10, 17, 25–6, 33, 35–6, 48, 60, 65, 88, 101 Peterffy, Thomas 38–9, 46, 58–9, 60, 65 pharmacogenomics 123 physics 28–9, 42, 51, 55, 72, 81, 92, 127–9 planning limitations 17, 21 politics 122, 132 Popper, Karl 19 portfolio managers 3, 6, 7, 45, 65–6, 74, 82–4, 87, 88, 98–100, 104, 129 positive feedback 91, 93 Powell, Colin 107 power laws 90–3, 97, 106, 134 predictions vii, viii, ix, xii, xiii, 3, 9, 17–18, 21, 28, 39–41, 57–8, 66, 85, 101–2, 111–13, 118– 36 Age of Prediction 123, 131–6 challenges 132–6 health 111–13, 118–22, 130–1 WorldQuant Initiative for Quantitative Prediction at Weill Cornell ix, xiii, xiv, 111–12, 118, 120–2, 132 see also alphas price of risk vii, 42, 66 price earnings ratios (PEs) 43 probabilities 9, 41–2, 59, 67–9, 72–7, 83–4, 87, 89 profit and loss statements 100 profits 60, 81–2, 84, 86, 100–1, 132 proteins 114–20 psychology of finance viii, 57–8, 72, 93 Puerto Rico 106–7 Python 105 QE II 53 quant biology viii, xi, 9, 109, 111–23 see also biology quant firms 4–10 ‘quant quake’ of August 2007 5–10, 17, 67, 133, 135 see also global financial crisis quantitative trading 3, 40, 42–3, 47–8, 67, 72–3, 81–93, 107, 111–13, 120–1, 130, 135–6 definition 3, 107 quantity/quality decisions 125, 129–30 quantum physics 28–9, 42, 51, 55, 72, 81, 92, 127–9 random walks 17, 40–2, 45–6, 56, 63, 93, 128 randomness 38–9, 40–2, 45, 51–2, 56, 57–9, 63, 73–5, 89, 92–3, 127–8, 133 reality 18, 19, 60, 65, 79, 113, 133–4 refuseniks 14–15 regrets 61 relative value positions 4, 6, 84 Remington Rand 44 Renaissance Technologies responsibilities 8, 61, 100 returns vii, viii, 5, 7, 42, 45, 65–7, 70, 75–7, 86, 101, 112, 132, 134 revalued positions 99 reversion price signals 83–4 Rhodes, Richard 29 Richmond, Virginia, author biography 24, 48 risk vii, viii, xii, 1, 4–7, 40–1, 42, 65–6, 68–71, 74, 81–2, 84–5 gains vii, viii, 5, 7, 42, 65–6, 75–7, 85–6 price of risk vii, 42, 66 take aggressive risks 1, 4–5, 7, 67 risk management xii, 4–5, 68–71, 74, 81–2, 84–5, 87, 100, 107 risk adjusted returns 66, 76 see also alpha values; Sharpe ratio RNA 111, 114–15, 117–18 robots 91, 102–3 rogue waves 52–6, 67–9, 90–1, 123 rules flaws 11, 17, 18–19, 72, 100–1, 107–8, 132, 136 overview of the rules 17–19, 88, 100, 133–4 see also UnRule concepts ruptures 57–8, 73, 132 Russell, Bertrand 19 Russia 4–5, 38, 61, 65, 67, 85–6, 101, 104–5 currency devaluations 4–5, 61, 65, 67, 85–6 privatizations 38 talent 38, 101, 104 see also Soviet Union S&P 500 Sakharov, Andrei 15 Sandra and Edward Meyer Cancer Center 121 Sanger, Frederick 116 scaling up 77, 79, 81–2, 88–93, 98, 101–2 definitions 81, 88, 89 Scholes, Myron 42, 46 Schrödinger, Erwin 51, 55–6, 127–9 securitization 70–1 see also mortgage backed securities self driving cars 102–3 self help programs 10 self reproducing automata 91 serotonin 119 Sette Mezzo 48 Shannon, Claude 35, 42, 56 Sharansky, Natan 15 Sharpe ratio 76, 108, 134 see also risk adjusted returns shipping lanes, the North Sea 52–3, 67–70, 123 Shleifer, Andrei 38 short positions, pairs trading strategies 60–1, 84–6 sickle cell anemia 122 signals xi, 17, 33, 35, 46–8, 65–7, 83–4, 89, 93, 99, 112–13, 119–20, 128–36 types 83–4, 119–20 see also momentum price signals; patterns; reversion price signals Silicon Valley 36 Simpson, O.J 59 simulations xii, 8, 9, 19, 27–30, 39–40, 58, 60, 72–7, 82–6, 98–9, 133–4 author biography 27–8, 39–40, 58, 60, 83–6, 98–9, 133–4 data requirements 30, 98–9, 103 neutralization strategies 85–6 uses 27, 29–30, 39–40, 60, 72–7, 83–4, 85, 98–9 see also back tests; WebSim software sinusoidal elements 54–5 skill types, talent 88, 101, 132, 135–6 smartphones 104, 130–1 Smith, Adam 27, 87 smokers 122 social media 99, 130–1 software tools 46, 67, 128–31 Solexa 115 soliton waves 55–6 Solzhenitsyn, Alexander 14–15 Sornette, Didier 57–8, 73, 91, 92–3, 128, 132 Soviet Union author biography vii, xi, 9, 10, 13–16, 19, 23–4, 36–8, 86, 135 collapse 38 schools 24 see also Russia specialization 87–8, 99–100 speculators 41, 71 Stanford 102–3 start ups 36, 38 statistical arbitrage 3, 4, 9, 59, 84 Stavridis, James 107 stochastic processes 41–2, 74 stock selections 37, 39, 40–1, 43–5, 60, 65, 82–5 Stradivari, Antonio 48 strengths 17, 61 subprime mortgage crisis 4, 6, 56–7, 71 see also global financial crisis success vii, viii, 5, 6–8, 10, 17–18, 19, 65–8, 72, 86–7, 88, 112 key factors 10, 17–18, 19, 65–7, 86, 87, 88 responsibilities 8, 61, 100 swarm intelligence 89 system accidents 69–70 systems theory 69–72, 91 Taiwan 37, 97, 104 take aggressive risks 1, 4–5, 7, 67 see also risk talent vii, viii, xii, xiii, 6–9, 13–14, 26, 38, 60, 82, 86–9, 97–106, 120–1, 132–6 hiring challenges 86–7, 97–100, 101–2, 104 opportunities vii, viii, xii, xiii, 6–9, 13–14, 26, 60, 86–7, 102–6, 132–5 power laws 97, 106 skill types 88, 101, 132, 135–6 see also employees; human capital tapestries 128 Tartaglia, Nunzio 60 technical traders (chartists) 46, 51 Teller, Edward 30 think big 7–8, 106–8, 109 Thrun, Sebastian 102–3 thymine, DNA nucleotides 114, 117 Timber Hill 38–9, 47, 58–9, 133 time series analysis 44, 57–8 traders, skills of good traders 88, 101 training/retraining benefits ix, 32, 100, 102, 106–7 traveling salesman problem 36 Treasury bills 76 tsunamis 53–4 turbulence, financial markets xiii, 7, 56–7, 93, 106 Turing, Alan 31–2, 74, 91–2, 114, 128 Udacity 102–3 Ulam, Stanislav 72–4, 91, 128 uncertainties 5, 17, 51, 83, 106, 134 United Nations 131 UNIVAC computers 44–5, 46 universal computers, historical background 31–2, 44, 91, 114 University of Pennsylvania 37–8, 58–9, 68, 72, 74 University of Southern California 73 University of Texas at Austin 32, 35–6 Unix 35 UnRule concepts xi, xii, xiii, 9–10, 11, 17–19, 72, 100–1, 107–8, 113, 132–4, 136 all theories and all methods have flaws rule 11, 18–19, 72, 100–1, 107–8, 132, 136 empirical aspects xii, 17, 19, 133–4 liar paradox 18–19 overview of rules 17–19, 133–4, 136 the UnRule that rules the rest 18–19, 100–1, 107–8, 132, 136 uracil, RNA 117 USA, author biography vii, xi, xii, xiii, 9, 10, 13–19, 23–7, 32, 35–48, 58–61, 65, 77, 81–7, 97, 106, 133–6 Value Line 43–6, 84–5 vectors, alphas 66–7, 100 venture capital 36 video games, author biography xi, xii, 9, 26–7, 133 violins 48, 51, 61 Virginia 24 viruses 113–14, 115, 118–19, 129 volatilities viii, 37, 65, 67, 76 Volkswagen 102–3 von Neumann, John (Johnny) 28–32, 42, 51, 57, 72, 73–4, 85, 91, 92, 128 Washington, George viii Watson, James 114 waves vii, viii, 9, 49, 51–61, 67–70, 123, 131 breathers 55–6 financial markets vii, viii, 9, 49, 51–2, 56–61, 67 the North Sea 52–3, 67–70, 123 one dimensional nonlinear Schrödinger equation (soliton waves) 55–6 Peregrine breathers 55–6 rogue waves 52–6, 67–9, 90–1, 123 types 51–7, 131 WebSim software 8, 98–9, 104, 106 see also back tests; simulations Weill Cornell ix, xiii, xiv, 111–12, 118, 120–2, 128, 132 West, Geoffrey 89 Western Electric 36 Wharton School at the University of Pennsylvania 37–8, 58–9, 68, 72 What Is Life? (Schrödinger) 127, 128–9 Whitehead, Alfred North 18–19 Wichita, Kansas, author biography xi, 24–5, 106 Wilkins, Maurice 114 World Economic Forum (WEF) 130–1 World War II 13, 28–31, 38, 72, 92, 127 WorldQuant ix, xii, xiii, xiv, 3–10, 17–18, 27, 36–8, 40, 67, 87–9, 93, 97–108, 111–12, 128– 30, 132–6 Challenge contest 8, 104 China 97–8, 100–1, 103, 105 core ideas 7–8, 17–18, 36, 67, 88–9, 93, 106–8, 120, 128–32, 133–5 education goals ix, xiii, 7, 8–9, 97–100, 102–5, 132–3 employees 7, 8–9, 38, 82, 87, 97–107, 120–1, 132–3, 135–6 global financial crisis 3–7, 17, 102 global summit of 2015 106–7 globalization 97–8, 100–6, 135–6 launch 88–9 Millennium Management spin off 97, 101–2 organizational structure 88–9, 107 origins 7, 10, 37, 87–9 ‘quant quake’ of August 2007 5–10, 17, 67, 133, 135 success 5, 6–8, 10, 67, 112 see also alphas WorldQuant Foundation ix, xiii, 8–9, 102–5, 111–12 WorldQuant Initiative for Quantitative Prediction at Weill Cornell ix, xiii, xiv, 111–12, 118, 120–2, 128, 132 WorldQuant University ix, xiii, 8–9, 102–3, 104–5, 111 Worley, Rick 32 WQSim 98 Yale University 68, 69, 92 yottabytes 118, 130 you have to think about it all the time motto 39 you only live once rule 17 YouTube 26 zettabytes 130 Zurich's Swiss Federal Institute of Technology 57 WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... successors lacked the mathematics and the computing power to begin to penetrate the complexity of even simple markets, the hand remained “invisible” and, for the most part, unpredictable The word “complex”... apartment and a car, and the two of them were able to what they loved My memories of Minsk are scattered: the city wrapped around the Svislach and Nyamiha rivers, the immense GUM department store, and. .. rising markets and losses in falling ones Then a contagion effect developed, with the stress in one part of the market spreading to others Prices fell, and the more they fell, the worse it got The