Lecturing Birds on Flying E-Book

Pablo Triana

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 717

Veröffentlichungsjahr: 2009

Beschreibung

LECTURING BIRDS ON FLYING For the past few decades, the financial world has often displayed an unreasonable willingness to believe that "the model is right, the market is wrong," in spite of the fact that these theoretical machinations were largely responsible for the stock market crash of 1987, the LTCM crisis of 1998, the credit crisis of 2008, and many other blow-ups, large and small. Why have both financial insiders (traders, risk managers, executives) and outsiders (academics, journalists, regulators, the public) consistently demonstrated a willingness to treat quantifications as gospel? Nassim Taleb first addressed the conflicts between theoretical and real finance in his technical treatise on options, Dynamic Hedging. Now, in Lecturing Birds on Flying, Pablo Triana offers a powerful indictment on the trustworthiness of financial theory, explaining--in jargon-free plain English--how malfunctions in these quantitative machines have wreaked havoc in our real world. Triana first analyzes the fundamental question of whether financial markets can in principle really be solved mathematically. He shows that the markets indeed cannot be tamed with equations, presenting a long and powerful list of obstacles to prove his point: maverick unlawful human actions rule the markets, unexpected and unimaginable events shape the markets, and historical data is not necessarily a trustworthy guide to the future of the markets. The author then examines the sources of origin of many prevalent theories and mathematical dictums. He details how the field of financial economics evolved from a descriptive discipline to an abstract one dedicated to technically concocting professors' own versions of how such a world should work. He goes on to explain how Wall Street and other financial centers became eager employers of scientists, and how scientists became eager employees of financial firms. Triana concludes with an in-depth discussion of the most significant historical episodes of theory-caused real-life market malaise, with a strong emphasis on the current credit crisis. In the end, Lecturing Birds on Flying calls for the radical substitution of good old-fashioned common sense in place of mathematical decision-making and the restoration to financial power of those who are completely unchained to the iron ball of classroom-obtained qualifications.

Leseprobe

Table of Contents

Title Page

Copyright Page

Dedication

Epigraph

Foreword

I

II

III

IV

V

VI

VII

Preface

Mathew Gladstein’s Complaisance

Essentials

Chapter 1 - Playing God

Chapter 2 - The Financial Economics Fiefdom

Chapter 3 - Quant Invasion

Critique

Chapter 4 - Copulated Nightmares

Chapter 5 - Blah VaR Blah

Chapter 6 - Blue Is Not Green

Chapter 7 - The Black-Scholes Conundrum

Conclusions

Chapter 8 - Black Swan Deceit?

Chapter 9 - An Unhealthy Yearning for Precision

Chapter 10 - We Need Fat Tony

Finale

Notes

Acknowledgements

About the Author

Index

Copyright © 2009 by Pablo Triana. All rights reserved.

Foreword © Nassim Nicholas Taleb.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Triana, Pablo.

Lecturing birds on flying : can mathematical theories destroy the financial markets? / Pablo Triana. p. cm.

Includes bibliographical references and index.

eISBN : 978-0-470-50105-4

1. Finance. 2. Economics. I. Title.

HG101.T75 2009

932-dc22

2009001895

To my parents, who gave me the perfect life.

Too large a proportion of mathematical economics are a mere concoction, as imprecise as the initial assumptions they rest on, which allow the author to lose sight of the complexities and interdependencies of the real world in a maze of pretentious and unhelpful symbols.

—John Maynard Keynes, 1936

Because of the success of science there is a kind of pseudo-science, social science isan example, which is not a science. They follow the forms, they gather data andso forth, but they don’t get any laws, they haven’t found anything, they haven’tgot anywhere (yet)...Maybe I am wrong, maybe they do know but I don’t thinkso, I have the advantage of having found out how hard it is to really get to knowsomething, how careful you have to be about checking the experiments, how easyit is to make mistakes. I know what it means to know something and thereforeI see how they get their information and I can’t believe that they have done theworks necessary, and the checks necessary, and the care necessary. I have a greatsuspicion, that they don’t know and that they are intimidating people. I don’tknow the world very well, but that’s what I think.

—Richard Feynman, 1981

Beware of geeks bearing formulas.

—Warren Buffett, 2008

Foreword

I

January 2009: I am at the World Economic Forum in Davos, looking at the sorry crowd of businessmen, journalists, and bankers. There are also a few finance academics. Many practitioners look like they have just fallen off a bicycle, still confused about how to behave. All these years, they had not realized that their models underestimated the risks of high-impact rare events, allowing the buildup of huge positions that are in the process of destroying free markets, capitalism, and finance. Instead of making probabilistic assessments about Black Swans, they should have insured some kind of robustness to them. I feel sorry for the crowd, as I am certain that most of these people will not be here next year—there is effectively a mechanism called evolution, harsh to humans.

But the academics among them, equally wrong about the models (in fact, they were the ones feeding bankers with bad models), wrong about the world, wrong about the very notion of knowledge, wrong about everything, will be back next year—that I guarantee. Unless they are caught seducing graduate assistants, their jobs are safe. Nobody ever lost his tenure in social science for being wrong (the opposite may be true). There is no such thing as evolution in academic settings.

II

The biggest myth I’ve encountered in my life is as follows: that the road from practical know-how to theoretical knowledge is reversible—in other words, that theoretical knowledge can lead to practical applications, just as practical applications can lead to theoretical knowledge. After all, this is the reason we have schools, universities, professors, research centers, homework, exams, essays, dissertations, and the strange brand of individuals called “economists.”

Yet the strange thing is that it is very hard to realize that knowledge cannot travel equally in both directions. It flows better from practice to theory—but to understand it you have nontheoretical knowledge. And people who have nontheoretical knowledge don’t think of these things.

Indeed, if knowledge flowed equally in both directions, then theory without experience should be equivalent to experience without theory—which is not the case.

The myth may have all started in a Plato dialogue, Euthyphro, in which Socrates heckled a fellow who claimed to be pious but could not define piety. The flustered fellow, bullied by Socrates, never replied (according to Plato) that babies drink their mother’s milk without being able to define what drinking milk is, or love their mother without being to explain what love or mother mean. This led to the thinking in the primacy and overblown importance of what can be called propositional knowledge—with so many side effects.

Alas, it took me a long time to disbelieve in propositional knowledge. Not only do you need to be a practitioner to realize it, but you need to ignore cultural opinions and use the raw, plain, easily obtainable, and somewhat shockingly potent evidence. And if you consider the effect for a moment, you will realize that it is more consequential than you thought.

Let me explain how the problem started screaming at me, around 1998. I was then sitting in a Chicago restaurant with a finance academic, though a true, thoughtful gentleman. He was advising one of the local exchanges on new products and wanted my opinion on the introduction of knock-out options—which I had covered in some detail in my first book, Dynamic Hedging. He recognized that the demand for these options was great, but wondered “how traders could handle these exotics if they do not understand the Girsanov theorem.” The Girsanov theorem is about a change of probability measure, something mathematically complicated that was needed to derive a closed-form formula for the options—though in the well-behaved Gaussian world. But you don’t need it to understand anything about exotic options. For a minute I wondered if I was living on another planet or if the gentleman’s PhD led to his strange loss of common sense—or if people without practical sense usually manage to get the energy to acquire a PhD in financial economics. Nobody worries that a child ignorant of the various theorems of thermodynamics and incapable of solving an equation of motion would be unable to ride a bicycle. Yet, why is it that we made the Euthyphro mistake with our understanding of quantitative products in the markets? Why should traders responding to supply and demand, little more, competing to make a buck, do the Girsanov theorem, any more than a trader of pistachios in the Souk of Damascus needs to solve general equilibrium equations to set the price of his product?

Then I realized that there has to be a problem with education—any form of formal education. I collected enough evidence that once you get a theory in your head, you can no longer understand how people can operate without it. And you look at practitioners, lecture them on how to do their business, and live under the illusion that they owe you their lives. Without your theories and your learning, they will never go anywhere.

All that can be tested. How? We can look at historical evidence. It is there, in front of our eyes, staring at us.

III

Let us take what is known as the Black-Scholes option pricing formula. Every person who had the misfortune of taking a finance class is under the illusion that the Black-Scholes-Merton formula is a gift from the three individuals who offered it to mankind and need to be rewarded for their great deed because we otherwise would not have the technology to understand these items. Without it we cannot price options. True?

Well, Espen Haug and I scratched the surface looking for the real evidence going back to the late nineteenth century. And we figured out that traders did much, much better pricing options before the option formulas were invented. The solid arbitrages were maintained (put-call parity, no negative butterfly, etc.). Traders, thanks to tinkering and evolutionary pressures, fumbled their way into a heuristic option pricing formula: Those who liked to short out-of-the-money options blew up in time; those who bought them survived. Traders knew what the heuristic “delta” was—about half for an at-the-money option, progressively less for an out-of-the-money option. Indeed, in our paper we interviewed veterans who confirmed that option traders in Chicago priced “off the butterfly,” with “no sheets” (i.e., no pricing formula). I myself was a pit trader in Chicago in the early 1990s and saw that prominent option traders priced options without formulas.

Traders were robust to the Black Swans, these sudden events that are the scourge of option traders.

In that respect, Black-Scholes-Merton was a dangerous regression. It was made only to accommodate the financial economics establishment and portfolio theory by showing how dynamic hedging removed the price of risk—a Platonic thought experiment that was beyond the unnecessary, as it proved toxic. The exact formula they used—narrowing down the distribution to the Gaussian—had been around in its exact form since Ed Thorpe and in a different, no less realistic form since Louis Bachelier, which could accommodate any probability distribution. Various accounts of the history of financial theory ignored the point: It is not just that history is written by the winners; it is written by the losers—those losers with access to the printing press, namely finance professors. I noted while reading a book by Mark Rubinstein how he stuck the names of finance professors on products we practitioners had been trading and perfecting at least a decade earlier. History written by the losers? A prime example is how the historian managed to downplay his “portfolio insurance,” a method that failed miserably in the crash of 1987.

History is truly written by losers with time on their hands and a protected academic position. In the greatest irony, the historical account of techné in derivatives pricing that Haug and I wrote was submitted in response to an invitation by an encyclopedia of quantitative finance. The editor of the historical section, proceeded to rewrite our story to reverse its message and glorify the epistemé crowd.

I was a trader and risk manager for almost 20 years (before experiencing battle fatigue). There is no way my and my colleagues’ accumulated knowledge of market risks can be passed on to the next generation. Business schools block the transmission of our practical know-how and empirical tricks, and the knowledge dies with us. We learn from crisis to crisis that modern financial theory has the empirical and scientific validity of astrology (without the aesthetics); yet the lessons are forgotten and ignored in what is taught to 150,000 business school students worldwide.

Note that what academics tend to call “practitioners” are often PhD-laden academics who go to practice and fall prey to the Euthyphro problem. This is why Pablo Triana was capable of writing this book: Like a minority of people (Espen Haug, myself), he did not go from theory to practice, but did the reverse.

IV

There is another problem with current researchers in financial economics: They are self-serving—perhaps no more, but certainly no less than other professions. Just as one of the problems with governments is that government officials have an objective function that may deviate from that of the general public, it is a myth, a great myth, that academics are there for the truth. When you hear a tobacco company talk about “health,” you smirk—yet when you hear a finance professor talk about “evidence” and “risk,” you don’t.

Alas, academics claim to look for evidence. But they seem to select what evidence they need for their purpose. I have shown that value at risk (VaR) does not work, with mathematical and empirical evidence (20 million pieces of data). But the evidence was ignored. In at least one instance, it was derided. Mandelbrot was completely ignored and his work was hidden from us. Had I shown that it worked, or had other academics produced evidence that fit their point, it would have been called “evidence” and published.

Traditionally charlatans have hidden themselves behind garb, institutions, and language. Now add fancy mathematics. Robert Merton’s book Continuous Time Finance contains 339 mentions of the word theorem (or equivalent). An average physics book of the same length has 25 such mentions. Yet, while economic models, it has been shown, work hardly better than random guesses or the intuition of cab drivers, physics can predict a wide range of phenomena with tenth-of-a-decimal precision.

They make you believe that their detractors are quacks by going ad hominem and skirting the arguments altogether. For a strange reason, I saw more solid critical thinking on the part of practitioners than academics. One common argument I’ve heard trying to extinguish my criticism of VaR in The Black Swan: “This is a popular book,” implying that its arguments lack rigor. Now if all arguments lacking in rigor are popular, it does not follow that all popular arguments are lacking in rigor. You rarely find people outside academia making such a mistake.

The cost of modelization is the loss of open-mindedness, but in some areas—say, engineering—this can be tolerated owing to the low-error quality of the models and their tracking ability.

My point is that that current academics are bad, but that there is a tendency by nonpractitioners to idealize Platonism and fall prey to the Euthyphro problem—not recognize that knowledge in society aggregates through action (a point made by Hayek but that did not sink in for the economics profession). While Pablo Triana is perhaps the very first person I’ve met who got the point, I highly disagree with his endorsement of the sterile critiques by nonpractitioners such as Derman and others, as their conscience-clearing halfwayness causes more harm than good. I hold that anything that does not start with the basis that techné (know- how) is superior to epistémé (know what), especially in complex systems, is highly suspicious.

V

One warning before concluding. You are often told, “This is just a model; it is just an aid; you do not need to use it.” I was often told that value at risk was just one piece of information among plenty—so these people providing it could cause no harm. True?

Do not put cigarettes in front of an addict—even if you give him a warning. I hold that information is not neutral. Never give a (fallible) human sterile information. He will not ignore it. These models led to an increase of risk in society, period. The providers are responsible.

VI

What should we do?

Do not waste time trying to convince academics. They will tell you, “Give me a better model or shut up,” not realizing that giving someone no map is much, much better than giving him a wrong map. Academia does not like negative advice (what not to do).

Just put them to shame. Ignore them. Put them down. Discredit business schools. Ask for cuts in funding.

We can no longer afford to compromise.

Do what some friends have done: resign from the various associations, such as the International Association of Financial Engineers and the CFA Institute. These institutions were promoting wrong models and will not repent naturally, no more than the tobacco industry would fight smoking in public places. You need to shame members, humiliate them. Make fun of these charlatans.

VII

I thank Pablo Triana for his wonderful lucidity, courage, and dedication in the service of the truth. This is the very first book that looks at the side effects of models, at the harm caused by models, and fearlessly points fingers where fingers should be pointed. I am convinced that the reader will come out of reading it much wiser, and that the publication of this book will make society a better, safer, and more risk-conscious place.

—NASSIM NICHOLAS TALEB

Preface

An Evening at NYU, Taleb’s Article, and a Credit Crisis

In December 2007 I was fortunate to attend a talk by legendary hedge fund honcho Jim Simons at one of my alma maters, New York University’s Stern School of Business. The founder of Renaissance Technologies (perhaps the best-performing hedge fund in the past decades, certainly one of the, if not the, über symbol of quantitative investing) answered several questions posed by moderator, and Nobel-prize winning econometrician, Robert Engle and by the house-packing audience on the performance of quant funds and the future for the markets.

Welcomingly, Simons offered pearly insights into his own background, the reasons that prompted a famed mathematician to abandon academia for Wall Street (his candor was remarkable: “I wanted to make more money more than other fellow scientists,” he shared), and how he runs his firm. He explained that the reason why Renaissance hired mostly (or only) scientists is that, not having had a financial background, it would have been difficult for him to judge the merits of a trader. But science, that he knew well, and he could tell if a mathematician or physicist was top or not. Another insightful moment came when Simons was asked to explain the notorious malaise that afflicted equity markets in August of that year. The quant legend confirmed what many had already explained: As funds began to suffer losses on mortgage-related stuff, they had to dump liquid assets (such as blue-chip stocks) so as to gather enough liquidity with which to meet incessant margin and redemption calls.

Illuminating as all those topics of discussion were, I was most intrigued by another particular remark that Simons made following a timely inquiry by Engle. “Why don’t you publish your research, the theory behind your trading methods?” went the question. “Maybe not while you are active in the markets, but perhaps later on?” After all, anyone would want to understand the quantitative philosophy behind such wildly successful quantitative trading.

Simons’ reply was that there is nothing to publish. It’s not physics. There is no fundamental, set-in-stone truth, no immutable laws in the markets. Financial truth changes every minute, so one would have to publish a new paper every week. In finance, the implication went, there is no eternal theorem that can help guide people through the ages. There can be no Einstein or Newton. Even the math genius raking in $1 billion a year and consistently generating 30 percent-plus returns wouldn’t qualify. The terrain, unlike in the physical world, is just too untameable and lawless.

I left Stern’s main auditorium quite perplexed. So the poster child of quant investing (in principle, the most sophisticated of punters out there) does not seem to believe that the markets can be theorized? That a general, overarching, all-encompassing model can capture financial behavior? And Simons, as he so eagerly clarified, walks the talk. In a hall packed with world-class professors and doctorate students (NYU is a consistent global leader in financial economics education and research), the revered mathematician loudly stated that he does not hire Finance academics. When you so obviously don’t believe in the theory, what’s the point of hiring the theorists?

Granted, Simons matched his matter-of-fact bluntness with graciousness towards his (by then slightly puzzled) hosts. As if to fill the void that his no-Finance-PhDs-please statement had suddenly left in the air, he promptly assuaged the audience by telling them that he was sure that they “were all going to do all right,” even if not be quite able to join the Renaissance ranks. But this, I felt, came as scant consolation for all those dogma-defending finance scholars in attendance who had just heard their presumptive hero tell them that their discipline is, after all, not a science. Sorry folks, it just ain’t string theory (which, by the way, Simons admitted to have never used when designing his moneymaking machines).

As I walked away from NYU and towards Manhattan’s magnificently youngish Union Square, in the pleasant company of a former fellow student from the good old days, I realized that something big had just happened. Since Renaissance is particularly secretive, it is essentially unassailable for outsiders to truly understand what the heck those brainiacs are actually doing, but there is no doubt as to the general consensus held (and this would apply to equally impressive quant houses like DE Shaw or Citadel): These people are making tons of money through the use of extraordinarily complex mathematical theories, comprehension of which is way beyond the reach of mere mortals. Thanks to their armies of elite scientists, quant funds can model the markets and predict their future behavior, goes the undoubted conventional wisdom. Vanilla investors who can’t mathematically map the markets stand no chance in comparison. In this day and age, conventionalist thinking would dictate, stochastic calculus and econometrics become the most essential tools for aspiring finance stars, with staid accounting and fundamental analysis consigned to the dustbin of unacceptable simplicity.