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The New Math

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Alpha magazine has a long article on the current state of quant finance. It may be sample bias, but former theoretical physicists predominate among the fund managers profiled.

I’ve always thought theoretical physics was the best training for applying mathematical techniques to real world problems. Mathematicians seldom look at data, so are less likely to have the all-important intuition for developing simple models of messy systems, and for testing models empirically. Computer scientists generally don’t study the broad variety of phenomena that physicists do, and although certain sub-specialties (e.g., machine learning) look at data, many do not. Some places where physics training can be somewhat weak (or at least uneven) include statistics, computation, optimization and information theory, but I’ve never known a theorist who couldn’t pick those things up quickly.

Physicists have a long record of success in invading other disciplines (biology, computer science, economics, engineering, etc. — I can easily find important contributions in those fields from people trained in physics, but seldom the converse). Part of the advantage might be pure horsepower — the threshold for completing a PhD in theoretical physics is pretty high. However, a colleague once pointed out that the standard curriculum of theoretical physics is basically a collection of the most practically useful mathematical techniques developed by man — the high points and greatest hits! Someone trained in that tradition can’t help but have an advantage over others when asked to confront a new problem.

Having dabbled in fields like finance, computer science and even biology, I’ve come to consider myself as a kind of applied mathematician (someone who applies mathematical ideas to the real world) who happens to have had most of his training from working on physical systems. I suspect that physicists who have left the field, as well as practitioners of biophysics, econophysics, etc. might feel the same way.

Readers of this blog sometimes accuse me of a negative perspective towards physics. Quite the contrary. Although I might not be optimistic about career prospects within physics, or the current state of the field, I can’t think of any education which gives a richer understanding of the world, or a greater chance of contributing to it.

…Finkelstein, who also grew up in Kharkov, has a Ph.D. in theoretical physics from New York University and a master’s degree in the same discipline from the Moscow Institute of Physics and Technology. Before joining Horton Point as chief science officer, he was head of quantitative credit research at Citadel Investment Group in Chicago.

Most of the 12 Ph.D.s at Horton Point’s Manhattan office are researching investment strategies and ways to apply scientific principles to finance. The firm runs what Finkelstein, 54, describes as a factory of strategies, with new models coming on line all the time. “It’s not like we plan to build ten strategies and sit on them,” he says. “The challenge is to keep it going, to keep this factory functioning.”

Along with his reservations about statistical arbitrage, Sogoloff is wary of quants who believe the real world is obliged to conform to a mathematical model. He acknowledges the difficulty of applying scientific disciplines like genetics or chaos theory — which purports to find patterns in seemingly random data — to finance. “Quantitative work will be much more rewarding to the scientist if one concentrates on those theories or areas that attempt to describe nonstable relationships,” he says.

Sogoloff sees promise in disciplines that deal with causal relationships rather than historical ones — like mathematical linguistics, which uses models to analyze the structure of language. “These sciences did not exist five or ten years ago,” he says. “They became possible because of humongous computational improvements.”

However, most quant shops aren’t exploring such fields because it means throwing considerable resources at uncertain results, Sogoloff says. Horton Point has found a solution by assembling a global network of academics whose research could be useful to the firm. So far the group includes specialists in everything from psychology to data mining, at such schools as the Beijing Institute of Technology, the California Institute of Technology and Technion, the Israel Institute of Technology.

Sogoloff tells the academics that the goal is to create the Bell Labs of finance. To align both parties’ interests, Horton Point offers them a share of the profits should their work lead to an investment strategy. Scientists like collaborating with Horton Point because it combines intellectual freedom with the opportunity to test their theories using real data, Sogoloff says. “You have experiments that can be set up in a matter of seconds because it’s a live market, and you have the potential for an amazing economic benefit.” …

Written by infoproc

April 7, 2008 at 4:12 am

The quants of August

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Why the rough markets in August? Why the big losses for certain quant funds? This paper claims the following:

1) many funds are pursuing the same strategies, using significant leverage

2) problems in the credit markets forced some multi-strategy funds to sell liquid equity positions in order to meet margin calls

3) positions commonly held by quant funds deteriorated in a correlated manner

Conclusion: systemic risk galore!

More discussion in the Economist.

What Happened to the Quants in August 2007?

AMIR KHANDANI
ANDREW W. LO
Massachusetts Institute of Technology

September 20, 2007

Abstract:

During the week of August 6, 2007, a number of high-profile and highly successful quantitative long/short equity hedge funds experienced unprecedented losses. Based on empirical results from TASS hedge-fund data as well as the simulated performance of a specific long/short equity strategy, we hypothesize that the losses were initiated by the rapid unwinding of one or more sizable quantitative equity market-neutral portfolios. Given the speed and price impact with which this occurred, it was likely the result of a sudden liquidation by a multi-strategy fund or proprietary-trading desk, possibly due to margin calls or a risk reduction. These initial losses then put pressure on a broader set of long/short and long-only equity portfolios, causing further losses on August 9th by triggering stop-loss and de-leveraging policies. A significant rebound of these strategies occurred on August 10th, which is also consistent with the sudden liquidation hypothesis. This hypothesis suggests that the quantitative nature of the losing strategies was incidental, and the main driver of the losses in August 2007 was the firesale liquidation of similar portfolios that happened to be quantitatively constructed. The fact that the source of dislocation in long/short equity portfolios seems to lie elsewhere – apparently in a completely unrelated set of markets and instruments – suggests that systemic risk in the hedge-fund industry may have increased in recent years.

Written by infoproc

November 2, 2007 at 1:29 am

Talkin’ bout my generation

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Michael Lewis, writing in the Times magazine, profiles John Seo, one of the new generation of quant hedge fund managers. Seo trades catastrophe bonds, instruments that let insurers and reinsurers transfer risk from natural disasters. An interesting point discussed later in the article is that the typical premium charged for rare event insurance (tail risk) is about 4-5 times the expected loss, and that this rough rule of thumb is found across many different kinds of risk.

Seo’s path to finance is a typical one for physicists in my generation, including the objections from his traditional Asian family 🙂 People often ask me why I am interested in quant finance. If the majority of friends you knew in college and graduate school (all of them brilliant and highly trained scientists) ended up doing something different than you, wouldn’t you naturally be curious about what they were up to? The most common sentiment I’ve heard expressed by former physicists who are now in finance is “I can’t believe I waited so long to leave”!

Whatever image pops to mind when you hear the phrase “hedge fund manager,” Seo (pronounced so) undermines it. On one hand, he’s the embodiment of what Wall Street has become: quantitative. But he’s quirky. Less interested in money and more interested in ideas than a Wall Street person is meant to be. He inherited not money but math. At the age of 14, in 1950, his mother fled North Korea on foot, walked through live combat, reached the United States and proceeded to become, reportedly, the first Korean woman ever to earn a Ph.D. in mathematics. His father, a South Korean, also came to the United States for his Ph.D. in math and became a professor of economic theory. Two of his three brothers received Ph.D.’s — one in biology, the other in electrical engineering. John took a physics degree from M.I.T. and applied to Harvard to study for his Ph.D. As a boy, he says, he conceived the idea that he would be a biophysicist, even though he didn’t really know what that meant, because, as he puts it, “I wanted to solve a big problem about life.” He earned his doctorate in biophysics from Harvard in three years, a department record.

His parents had raised him to think, but his thoughts were interrupted once he left Harvard. His wife was pregnant with their second child, and the health plan at Brandeis University, where he had accepted a job, declared her pregnancy a pre-existing condition. He had no money, his parents had no money, and so to cover the costs of childbirth, he accepted a temp job with a Chicago trading firm called O’Connor and Associates. O’Connor had turned a small army of M.I.T. scientists into options traders and made them rich. Seo didn’t want to be rich; he just wanted health insurance. To get it, he agreed to spend eight weeks helping O’Connor price esoteric financial options. When he was done, O’Connor offered him 40 grand and asked him to stay, at a starting salary of $250,000, 27 times his post-doc teaching salary. “Biophysics was starved for resources,” Seo says. “Finance was hurling resources at problems. It was almost as if I was taking it as a price signal. It was society’s way of saying, Please, will you start solving problems over here?”

His parents, he suspected, would be appalled. They had sacrificed a lot for his academic career. In the late 1980s, if you walked into the Daylight Donuts shop in Dallas, you would have found a sweet-natured Korean woman in her early 50s cheerfully serving up honey-glazed crullers: John’s mom. She had abandoned math for motherhood, and then motherhood for doughnuts, after her most promising son insisted on attending M.I.T. instead of S.M.U., where his tuition would have been free. She needed money, and she got it by buying this doughnut shop and changing the recipe so the glaze didn’t turn soggy. (Revenues tripled.) Whatever frustration she may have felt, she hid, as she did most of her emotions. But when John told her that he was leaving the university for Wall Street, she wept. His father, a hard man to annoy, said, “The devil has come to you as a prostitute and has asked you to lie down with her.”

A willingness to upset one’s mother is usually a promising first step to a conventional Wall Street career. But Seo soon turned Wall Street into his own private science lab, and his continued interest in deep questions mollified even his father. “Before he got into it, I strongly objected,” Tae Kun Seo says. “But now I think he’s not just grabbing money.” He has watched his son quit one firm to go to work for another, but never for a simple promotion; instead, John has moved to learn something new. Still, everywhere he goes, he has been drawn to a similar thorny problem: the right price to charge to insure against potential losses from extremely unlikely financial events. “Tail risk,” as it is known to quantitative traders, for where it falls in a bell-shaped probability curve. Tail risk, broadly speaking, is whatever financial cataclysm is believed by markets to have a 1 percent chance or less of happening. In the foreign-exchange market, the tail event might be the dollar falling by one-third in a year; in the bond market, it might be interest rates moving 3 percent in six months; in the stock market, it might be a 30 percent crash. “If there’s been a theme to John’s life,” says his brother Nelson, “it’s pricing tail.”

And if there has been a theme of modern Wall Street, it’s that young men with Ph.D.’s who approach money as science can cause more trouble than a hurricane. John Seo is oddly sympathetic to the complaint. He thinks that much of the academic literature about finance is nonsense, for instance. “These academics couldn’t understand the fact that they couldn’t beat the markets,” he says. “So they just said it was efficient. And, ‘Oh, by the way, here’s a ton of math you don’t understand.’ ” He notes that smart risk-takers with no gift for theory often end up with smart solutions to taking extreme financial risk — answers that often violate the academic theories. (“The markets are usually way ahead of the math.”) He prides himself on his ability to square book smarts with horse sense. As one of his former bosses puts it, “John was known as the man who could price anything, and his pricing felt right to people who didn’t understand his math.”

Written by infoproc

August 26, 2007 at 5:05 pm

Posted in hedge funds, physics, quants

Talkin’ bout my generation

with 3 comments

Michael Lewis, writing in the Times magazine, profiles John Seo, one of the new generation of quant hedge fund managers. Seo trades catastrophe bonds, instruments that let insurers and reinsurers transfer risk from natural disasters. An interesting point discussed later in the article is that the typical premium charged for rare event insurance (tail risk) is about 4-5 times the expected loss, and that this rough rule of thumb is found across many different kinds of risk.

Seo’s path to finance is a typical one for physicists in my generation, including the objections from his traditional Asian family 🙂 People often ask me why I am interested in quant finance. If the majority of friends you knew in college and graduate school (all of them brilliant and highly trained scientists) ended up doing something different than you, wouldn’t you naturally be curious about what they were up to? The most common sentiment I’ve heard expressed by former physicists who are now in finance is “I can’t believe I waited so long to leave”!

Whatever image pops to mind when you hear the phrase “hedge fund manager,” Seo (pronounced so) undermines it. On one hand, he’s the embodiment of what Wall Street has become: quantitative. But he’s quirky. Less interested in money and more interested in ideas than a Wall Street person is meant to be. He inherited not money but math. At the age of 14, in 1950, his mother fled North Korea on foot, walked through live combat, reached the United States and proceeded to become, reportedly, the first Korean woman ever to earn a Ph.D. in mathematics. His father, a South Korean, also came to the United States for his Ph.D. in math and became a professor of economic theory. Two of his three brothers received Ph.D.’s — one in biology, the other in electrical engineering. John took a physics degree from M.I.T. and applied to Harvard to study for his Ph.D. As a boy, he says, he conceived the idea that he would be a biophysicist, even though he didn’t really know what that meant, because, as he puts it, “I wanted to solve a big problem about life.” He earned his doctorate in biophysics from Harvard in three years, a department record.

His parents had raised him to think, but his thoughts were interrupted once he left Harvard. His wife was pregnant with their second child, and the health plan at Brandeis University, where he had accepted a job, declared her pregnancy a pre-existing condition. He had no money, his parents had no money, and so to cover the costs of childbirth, he accepted a temp job with a Chicago trading firm called O’Connor and Associates. O’Connor had turned a small army of M.I.T. scientists into options traders and made them rich. Seo didn’t want to be rich; he just wanted health insurance. To get it, he agreed to spend eight weeks helping O’Connor price esoteric financial options. When he was done, O’Connor offered him 40 grand and asked him to stay, at a starting salary of $250,000, 27 times his post-doc teaching salary. “Biophysics was starved for resources,” Seo says. “Finance was hurling resources at problems. It was almost as if I was taking it as a price signal. It was society’s way of saying, Please, will you start solving problems over here?”

His parents, he suspected, would be appalled. They had sacrificed a lot for his academic career. In the late 1980s, if you walked into the Daylight Donuts shop in Dallas, you would have found a sweet-natured Korean woman in her early 50s cheerfully serving up honey-glazed crullers: John’s mom. She had abandoned math for motherhood, and then motherhood for doughnuts, after her most promising son insisted on attending M.I.T. instead of S.M.U., where his tuition would have been free. She needed money, and she got it by buying this doughnut shop and changing the recipe so the glaze didn’t turn soggy. (Revenues tripled.) Whatever frustration she may have felt, she hid, as she did most of her emotions. But when John told her that he was leaving the university for Wall Street, she wept. His father, a hard man to annoy, said, “The devil has come to you as a prostitute and has asked you to lie down with her.”

A willingness to upset one’s mother is usually a promising first step to a conventional Wall Street career. But Seo soon turned Wall Street into his own private science lab, and his continued interest in deep questions mollified even his father. “Before he got into it, I strongly objected,” Tae Kun Seo says. “But now I think he’s not just grabbing money.” He has watched his son quit one firm to go to work for another, but never for a simple promotion; instead, John has moved to learn something new. Still, everywhere he goes, he has been drawn to a similar thorny problem: the right price to charge to insure against potential losses from extremely unlikely financial events. “Tail risk,” as it is known to quantitative traders, for where it falls in a bell-shaped probability curve. Tail risk, broadly speaking, is whatever financial cataclysm is believed by markets to have a 1 percent chance or less of happening. In the foreign-exchange market, the tail event might be the dollar falling by one-third in a year; in the bond market, it might be interest rates moving 3 percent in six months; in the stock market, it might be a 30 percent crash. “If there’s been a theme to John’s life,” says his brother Nelson, “it’s pricing tail.”

And if there has been a theme of modern Wall Street, it’s that young men with Ph.D.’s who approach money as science can cause more trouble than a hurricane. John Seo is oddly sympathetic to the complaint. He thinks that much of the academic literature about finance is nonsense, for instance. “These academics couldn’t understand the fact that they couldn’t beat the markets,” he says. “So they just said it was efficient. And, ‘Oh, by the way, here’s a ton of math you don’t understand.’ ” He notes that smart risk-takers with no gift for theory often end up with smart solutions to taking extreme financial risk — answers that often violate the academic theories. (“The markets are usually way ahead of the math.”) He prides himself on his ability to square book smarts with horse sense. As one of his former bosses puts it, “John was known as the man who could price anything, and his pricing felt right to people who didn’t understand his math.”

Written by infoproc

August 26, 2007 at 5:05 pm

Posted in hedge funds, physics, quants

Talkin’ bout my generation

leave a comment »

Michael Lewis, writing in the Times magazine, profiles John Seo, one of the new generation of quant hedge fund managers. Seo trades catastrophe bonds, instruments that let insurers and reinsurers transfer risk from natural disasters. An interesting point discussed later in the article is that the typical premium charged for rare event insurance (tail risk) is about 4-5 times the expected loss, and that this rough rule of thumb is found across many different kinds of risk.

Seo’s path to finance is a typical one for physicists in my generation, including the objections from his traditional Asian family 🙂 People often ask me why I am interested in quant finance. If the majority of friends you knew in college and graduate school (all of them brilliant and highly trained scientists) ended up doing something different than you, wouldn’t you naturally be curious about what they were up to? The most common sentiment I’ve heard expressed by former physicists who are now in finance is “I can’t believe I waited so long to leave”!

Whatever image pops to mind when you hear the phrase “hedge fund manager,” Seo (pronounced so) undermines it. On one hand, he’s the embodiment of what Wall Street has become: quantitative. But he’s quirky. Less interested in money and more interested in ideas than a Wall Street person is meant to be. He inherited not money but math. At the age of 14, in 1950, his mother fled North Korea on foot, walked through live combat, reached the United States and proceeded to become, reportedly, the first Korean woman ever to earn a Ph.D. in mathematics. His father, a South Korean, also came to the United States for his Ph.D. in math and became a professor of economic theory. Two of his three brothers received Ph.D.’s — one in biology, the other in electrical engineering. John took a physics degree from M.I.T. and applied to Harvard to study for his Ph.D. As a boy, he says, he conceived the idea that he would be a biophysicist, even though he didn’t really know what that meant, because, as he puts it, “I wanted to solve a big problem about life.” He earned his doctorate in biophysics from Harvard in three years, a department record.

His parents had raised him to think, but his thoughts were interrupted once he left Harvard. His wife was pregnant with their second child, and the health plan at Brandeis University, where he had accepted a job, declared her pregnancy a pre-existing condition. He had no money, his parents had no money, and so to cover the costs of childbirth, he accepted a temp job with a Chicago trading firm called O’Connor and Associates. O’Connor had turned a small army of M.I.T. scientists into options traders and made them rich. Seo didn’t want to be rich; he just wanted health insurance. To get it, he agreed to spend eight weeks helping O’Connor price esoteric financial options. When he was done, O’Connor offered him 40 grand and asked him to stay, at a starting salary of $250,000, 27 times his post-doc teaching salary. “Biophysics was starved for resources,” Seo says. “Finance was hurling resources at problems. It was almost as if I was taking it as a price signal. It was society’s way of saying, Please, will you start solving problems over here?”

His parents, he suspected, would be appalled. They had sacrificed a lot for his academic career. In the late 1980s, if you walked into the Daylight Donuts shop in Dallas, you would have found a sweet-natured Korean woman in her early 50s cheerfully serving up honey-glazed crullers: John’s mom. She had abandoned math for motherhood, and then motherhood for doughnuts, after her most promising son insisted on attending M.I.T. instead of S.M.U., where his tuition would have been free. She needed money, and she got it by buying this doughnut shop and changing the recipe so the glaze didn’t turn soggy. (Revenues tripled.) Whatever frustration she may have felt, she hid, as she did most of her emotions. But when John told her that he was leaving the university for Wall Street, she wept. His father, a hard man to annoy, said, “The devil has come to you as a prostitute and has asked you to lie down with her.”

A willingness to upset one’s mother is usually a promising first step to a conventional Wall Street career. But Seo soon turned Wall Street into his own private science lab, and his continued interest in deep questions mollified even his father. “Before he got into it, I strongly objected,” Tae Kun Seo says. “But now I think he’s not just grabbing money.” He has watched his son quit one firm to go to work for another, but never for a simple promotion; instead, John has moved to learn something new. Still, everywhere he goes, he has been drawn to a similar thorny problem: the right price to charge to insure against potential losses from extremely unlikely financial events. “Tail risk,” as it is known to quantitative traders, for where it falls in a bell-shaped probability curve. Tail risk, broadly speaking, is whatever financial cataclysm is believed by markets to have a 1 percent chance or less of happening. In the foreign-exchange market, the tail event might be the dollar falling by one-third in a year; in the bond market, it might be interest rates moving 3 percent in six months; in the stock market, it might be a 30 percent crash. “If there’s been a theme to John’s life,” says his brother Nelson, “it’s pricing tail.”

And if there has been a theme of modern Wall Street, it’s that young men with Ph.D.’s who approach money as science can cause more trouble than a hurricane. John Seo is oddly sympathetic to the complaint. He thinks that much of the academic literature about finance is nonsense, for instance. “These academics couldn’t understand the fact that they couldn’t beat the markets,” he says. “So they just said it was efficient. And, ‘Oh, by the way, here’s a ton of math you don’t understand.’ ” He notes that smart risk-takers with no gift for theory often end up with smart solutions to taking extreme financial risk — answers that often violate the academic theories. (“The markets are usually way ahead of the math.”) He prides himself on his ability to square book smarts with horse sense. As one of his former bosses puts it, “John was known as the man who could price anything, and his pricing felt right to people who didn’t understand his math.”

Written by infoproc

August 26, 2007 at 5:05 pm

Posted in hedge funds, physics, quants

Derman: How I became a quant

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Emanuel Derman reviews the new book HOW I BECAME A QUANT by Richard R. Lindsey and Barry Schachter.

Nice quote on Derman’s blog here:

It always seemed to me, and recent occur[r]ences seem to confirm it, that most algorithmic trading strategies are long volatility but short volatility of volatility.

A previous post from this blog: On the volatility of volatility

WSJ: In 1985, when I left academia and began putting my physics training to work on Wall Street, I talked eagerly about options theory to anyone who would listen. One lunchtime, I turned to a colleague in the elevator and began to babble about “convexity,” a mathematical property of options crucial to the Black-Scholes theory used in derivatives pricing. My friend clearly understood convexity, but he shuffled his feet uncomfortably and quickly changed the subject. “Hey, futures dropped more than a handle today!” he said, imitating a genuine bond trader. It didn’t take me long to recognize the source of his discomfort: I had just outed him as a fellow quant. Except back then we practitioners of quantitative finance didn’t refer to ourselves as quants. That’s what “real businesspeople” — traders, investment bankers, salespeople — called us, somewhat pejoratively.

Now the term is proudly embraced, as demonstrated by “How I Became a Quant,” which collects 25 mini-memoirs of academics who successfully made the jump to Wall Street. Quantitative finance might have lost a little of its luster in recent weeks with the sub-prime mortgage meltdown and its subsequent deleterious consequences for quantitative trading strategies, but quants know — as many of them in this book emphasize — that however science- and math-based investment calculations might be, there is still an art to their use and plenty of room for error.

But definitions first. What is a quant, or, rather, quantitative finance? It is an interdisciplinary mix that combines math, statistics, physics-inspired models and computer science, all aimed at the valuation and management of portfolios of financial securities. In practice, for example, a quant might be presented with a convertible bond being issued by a corporation and, by extending the Black-Scholes model to convertible securities, calculate its probable value. Or he might develop a quantitative algorithm to buy theoretically cheap stocks and short theoretically rich ones.

By my reckoning, several of the 25 memoirists in “How I Became a Quant” are not true quants, and they are honest (or proud) enough to admit it. But many others are renowned in the quant community. To name just a few: Ron Kahn, co-author of the classic “Active Portfolio Management”; Peter Carr, an options expert at Bloomberg; Cliff Asness, one of the founders of AQR Capital; and Peter Muller, who ran statistical arbitrage at Morgan Stanley.

Most of the book’s contributors belong to the first wave of a financial revolution that began in the 1970s, when interest rates soared, listed equity options grew popular and options traders began to rely on the mathematically sophisticated Black-Scholes model. Investment banks needed mathematical talent, and, as the academic job market dried up, physicists needed jobs. Many early quants were therefore physicists, amateurs who had happily entered a field that didn’t yet have a name.

Today we are in the middle of a second wave. As markets became increasingly electronic-based, asset and hedge-fund managers began to embrace algorithmic trading strategies — and started competing to hire quants, hoping to emulate the continuing successes of such firms founded in the 1980s as Renaissance Technologies and D.E. Shaw & Co. The establishment of the International Association of Financial Engineers, co-founded in 1992 by another contributor to this book, Jack Marshall, has further legitimized the field. Nowadays you can pay $30,000 a year or more to get a master’s degree in the subject. Financial engineering has become a profession, and amateurs are sadly passé.

Most of the early quants — in addition to physicists, they included computer scientists, mathematicians and economists — came to the field by force of circumstance. Even if they had been fortunate enough to find a secure academic position, they often became weary of the isolating academic grind and found that they liked working at investment banks and financial institutions. As former SAC Capital Management quant Neil Chriss notes, Wall Street is no more competitive than academia. Life in finance is often more collegial than college life itself — and more stimulating. It is impressive how many of the contributors here cite with awe their encounters with the late economist Fischer Black (1938-95), himself a Ph.D. in applied mathematics rather than economics, who always insisted that research on Wall Street was better than research in universities.

The memoirs in this book are not quite representative. That there are only two women contributors is proportionately accurate; most quants were male. But most quants were also foreign-born. When I ran an equity quant group in the 1990s, the great majority — all with doctorates — were from Europe, India or China. Only two of the memoirists grew up abroad in non-English-speaking countries. Quants in the second wave are still largely foreign-born, but more are women and fewer hold doctorates.

Several contributors to “How I Became a Quant” stress an essential point: Physics and finance are only superficially similar. While theoretical physics captures the essence of the material world to an accuracy of 10 significant figures, theoretical finance is at best an untrustworthy, limited representation of the mysterious way in which financial value is determined. Yet Thomas Wilson, the chief insurance risk officer of the ING Group, wisely remarks: “A model is always wrong, but not useless.” Despite the inadequacies of quantitative finance, we have nothing better. And, on the practical side, Andrew Sterge, the chief executive of AJ Sterge Investment Strategies, writes: “The greatest research in the world does no good if it cannot be implemented.”

Quants do get more respect these days, because their imperfect models can generate profits when used with a knowledge of their limitations. But quants can also produce awe-inspiring disasters when they begin to idolize their man-made models. Nevertheless, most quants, unless they have their own operations, are still second-class citizens on Wall Street rather than its superstars, and many still aspire to leave behind bookish mathematics and join the ranks of the “real businesspeople” who used to look down on them.

Written by infoproc

August 23, 2007 at 4:48 pm

Quant resume

with 8 comments

Anyone looking to hire a PhD quant? Someone I know is moving from theoretical physics to finance. Below is a partial resume. The candidate is 100% fluent in both English and Mandarin, easygoing, hardworking and a nice guy. I’ve edited a little to preserve privacy.

I guarantee you, he is at least as smart as this guy.

For you youngsters in physics, please read this.

Quantitative Qualifications

9+ years of advanced experience in Monte Carlo simulation of complex processes and parameter determination

7+ years of experience in constructing models to fit to a large data set and explain anomalous phenomena

Financial readings: John Hull’s “Options, Futures, and Other Derivatives”, Martin Baxter and Andrew Rennie’s “Financial Calculus”, and Salih Neftci’s “Introduction to Mathematics of Financial Derivatives”

Familiar with binomial models, Ito’s lemma, Black-Scholes, interest rate derivatives, bond pricing, volatility estimation, modern portfolio theory, and CAPM

Solid knowledge of mathematics and statistical techniques: Monte Carlo simulation, Linear Regression, Maximum Likelihood Method, Neural Networks, Linear Programming Methods, and Fourier Analysis

Proficiency in FORTRAN, Mathematica, Matlab and FORM. Working knowledge of C++, Java, and Perl

Education

Ph. D. in Theoretical Physics, University of Wisconsin, Madison (UW), August 2001, GPA: 3.9/4.0, with Mathematics minor

Professional Experiences

2004-Present: Postdoctoral research associate at [major research university]

Proposed and constructed extensions to the Standard Model of particle physics to fit to a very large set of experimental data, using linear regression

Used maximum likelihood method to constrain parameters of a new force carrier

Numerically solved correlated second order differential equations for renormalization flows to explain the convergence of three gauge interactions

Calculated resonance production cross sections of the Majorana neutrinos with Importance Sampling Monte Carlo integration

Conjectured a new weakly interacting particle to explain an anomaly in experimental data and suggested new processes to test the conjecture

2001-2004: Postdoctoral research associate at the Argonne National Laboratory, Argonne, IL

Performed a state of the art high order calculation of the production probability of a supersymmetric (SUSY) particle. Combined both analytical and numerical evaluations to achieve singularity cancellation and integrated over multi-dimensional phase space using Monte Carlo

Proposed new ways of breaking large Lie groups in higher space dimensional models and constructed the first dynamical breaking of SO(10) group via deconstruction

Designed the best way to determine one of the most important SUSY parameters with different experimental input

1997-2001: Research assistant at the University of Wisconsin, Madison, WI

Led several projects in calculating production probabilities of Higgs boson and SUSY particles

Wrote the standard reference in determining the SUSY charge and parity violating phase through electric dipole moments. Sampling of up to 26 dimensional parameter space was done using Monte Carlo simulation

Solved Einstein’s equation in high dimensional Anti de Sitter space

Publications

28 scientific papers published in top international journals, with more than 590 citations

4 papers in conference proceedings

Written by infoproc

August 7, 2007 at 1:28 am

Posted in finance, physics, quants