L2M: Lab to Market

Oregon’s Lab2Market Initiative – National Science Foundation Partnerships for Innovation Program

I’ll be giving a talk on Thursday, August 30 at the Lab2Market Entrepreneurship Workshop in Portland. I’ll post a link to the slides when they are ready.

Talkin’ bout my generation

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.”

Talkin’ bout my generation

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.”

Talkin’ bout my generation

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.”

Female faces

Two interesting videos, showing female faces in 500 years of Western art and in film.

I find both compilations visually fascinating. It is amazing how similar these nearly ideal female faces are to each other. We have pretty sophisticated hardwired capabilities for face-recognition; an alien species would probably conclude that we all look alike!

Derman: How I became a quant

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.

Fisher on credit meltdown

The WSJ interviews Peter Fisher, the man who led the Fed intervention during the LTCM crisis. He’s now an MD at Blackrock.

WSJ: What similarities or differences do you see to previous market crises?

Mr. Fisher: Big market events that pose systemic risks tend to reflect collective mistakes in which most market participants are offside in the same direction. In the summer of 1998 there was a collective misunderstanding about credit risk: Everyone underestimated sovereign risk and lived in the fantasyland were sovereigns did not default. Remember? “Russia won’t default, they have missiles.” It turned out not be about missiles but about cash flows. It turned out that credit mattered and then we had to revalue a lot of sovereign paper that was being used as collateral.

WSJ: So in 1998, there were problems with collateral. And this time, there are even more problems with collateral, right?

Mr. Fisher: Yes, indeed. Until the week before last, nobody seemed to be focused on the uncertainty surrounding the value of mortgage-related and structured-finance paper and, then, suddenly, everyone did. The late MIT Professor Rudi Dornbusch sagely observed that in financial markets things always take longer to happen than you expect but once they happen, events unfold much more quickly than you expect and this perfectly describes the events of mid-August.

WSJ: The conventional wisdom was that globalization would lead to a dispersion of risk. And yet, the market seems so spooked with announcements of problems from Australia to Germany as well as in the U.S. How do you see the costs and benefits of globalization in the financial markets?

Mr. Fisher: The benefit of course is risk diversification and dispersion but this comes with an offsetting cost. This is the cost of proxy or imperfect hedging, where market participants sell what they can rather than what they wish, which leads to higher linkages and less benefit of dispersion. In 1998, after Russia’s default, there was selling pressure in Mexican bonds not because the market thought a Mexican default was likely but because the Mexican bonds were liquid.

WSJ: What else feels different this time?

Mr. Fisher: In September of 1998 there were a lot fewer people who thought they saw a buying opportunity — famously, in the case of LTCM, only Warren Buffett and Hank Greenberg. It took until October and November of that year for more people to see a buying opportunity and for the markets to find a bottom.

Unparticles?

New paper!

http://arxiv.org/abs/0708.2735

Long range forces and limits on unparticle interactions

N.G. Deshpande, Stephen D.H. Hsu, Jing Jiang

Abstract:
Couplings between standard model particles and unparticles from a nontrivial scale invariant sector can lead to long range forces. If the forces couple to quantities such as baryon or lepton (electron) number, stringent limits result from tests of the gravitational inverse square law. These limits are much stronger than from collider phenomenology and astrophysics.

Blame the quants!

I can already see who the scapegoats will be for the subprime credit meltdown…

The mathematical models involved here are used to value bundles of mortgages or other debt, including corporate “junk” (high yield) bonds. Most importantly, they predict probabilities or rates of default based on historical data and the characteristics of the overall economy, the borrowers, etc. One problem is that rating agencies such as Moody’s and S&P were willing to rate senior tranches of subprime debt as AAA (safe), based on the model predictions. In other words, their models predicted that only the riskier tranches would take significant losses and sufficiently senior tranches were as safe as, well, T-bills.

Now, the failure of default models based on historical data might have something to do with loosening of credit standards and outright fraud at the mortgage broker (mainstreet) level. That has little to do with eggheads and math, although perhaps the eggheads should have realized the frailty of human nature in advance 🙂 Also, there is some question as to whether S&P and Moody’s were happy to nudge the ratings higher in order to drum up business. It is an inherent conflict of interest that ratings agencies are paid to generate ratings!

The second model problem is more subtle and plays a role in hedge fund strategies. The models predict relative changes in valuation in different tranches. If interest rates spike, or spreads change, the effect on the senior tranches might be very different from that on junior tranches or on equities. Hedge funds made bets on the correlations predicted by their models, but at least in the short term got into trouble because the market was indiscriminate in marking down all forms of credit, independent of quality. These trades may, in the long run, be big winners if the hedgies have sufficient liquidity to ride them out. Goldman (and some smart co-investors like Hank Greenberg and Eli Broad) and Citadel may have the brains, guts and liquidity to ride this out.

The credit industry is in the early stages of building a system to redistribute risk. This works quite well for us in, e.g., the insurance industry. But it would be naive to think that there won’t be hiccups and crises along the way. At the moment, much of the problem is fear and contagion: the system is new and untested, and the participants are afraid.

Final comment: I doubt the typical market neutral quant long-short fund is directly involved with credit products. They lost money recently simply because the market moved in a very unpredictable way — certain funds that did have credit exposure had to sell whatever liquid positions they had to make margin calls. That means stocks that quant models tended to favor suddenly and unexpectedly went way down…

For Wall Street’s Math Brains, Miscalculations

Complex Formulas Used by ‘Quant’ Funds Didn’t Add Up in Market Downturn

By Frank Ahrens
Washington Post
Tuesday, August 21, 2007; A01

They are the powerful, cerebral and offstage actors of Wall Street, but the recent turmoil in the financial markets has yanked them into the light.

They are the math geniuses of the quant funds.

Short for “quantitative equity,” a quant fund is a hedge fund that relies on complex and sophisticated mathematical algorithms to search for anomalies and non-obvious patterns in the markets. These glitches, often too small for the human eye, can present opportunities for short-and long-term trades that yield high-profit returns.

The models replace instinct. They try to turn historical trends into predictive science, using elegant mathematics seemingly above the comprehension of your average 401(k) participant or Wall Street fund manager.

Instead of veteran, market-savvy traders waving fistfuls of sell slips, the elite quant funds employ Nobel nerds with math PhDs, often divorced from the real world. It’s not for nothing that they are called “black-box” funds — opaque to outsiders, the boxes contain investment magic understood by only the wizards who conjured it up.

But the 387-point drop in the Dow Jones industrial average Aug. 9 and the continuing turmoil in the markets, in part attributed to massive sell-offs by the quant funds, have tarnished some of the quants’ glimmering intellectual credentials and shown that, when push comes to shove, they can rush toward the exits as fast as a novice investor.

Last week, Goldman Sachs said its Global Alpha quant fund had lost 27 percent of its value this year because its computers failed to anticipate what the firm called “25 percent standard deviation moves” or events so rare Goldman had seen them only twice before in the firm’s history. On the same day Goldman revealed the bad news, the firm said it would lead a group of big-money investors, including philanthropist Eli Broad, in pouring $3.6 billion into another Goldman quant fund, aiming to shore up confidence in the quants.

Barclays Global Investors, with $450 billion of its $2 trillion in assets under quant management, began applying mathematical tools to its funds in 1978. Last week, Barclays spokesman Lance Berg said the firm was “maintaining its investment process” despite the recent troubles. He would not say how much the Barclays quant funds had fluctuated during the period of turmoil.

The acknowledged quant king is James Simons, 69, an M.I.T.-trained mathematician with a groundbreaking theory that physicists are using to plumb the mysteries of superstring study and get at the very nature of
existence itself. Simons turned his big brain on investing after his math career, founding Renaissance Technologies quant shop. The firm pocketed $1.7 billion in investor fees last year, among the highest in the industry. In return, his clients can reap annual returns of more than 30 percent, according to news reports.

As elegant as the models are, they cannot predict unpredictable events, or human panic, some traders say. Further, some say, too many quant funds are full of myopic brainiacs, overly reliant on their tools.

“Most are idiot savants brought to industrial proportion,” Nassim Nicholas Taleb, former quant-jock and bestselling contrarian author, said by phone from Scotland, where he is promoting his new book on improbability, “The Black Swan.”

“They are very smart in front of a textbook but not smart enough to understand very elementary things in reality,” he said.

Taleb believes in monkey-wrench events that shatter the models of the quant-jocks. He says their algorithms don’t adequately account for huge, rare anomalies, such as the current surprise credit crunch. Or the Russian credit crisis in 1998 that nearly put the superstar quant fund of the time, Long-Term Capital Management, out of business in a matter of days, saved by cash infusion organized by the Federal Reserve.

The sentiment is reminiscent of the demise of Enron, a company said to have been designed by geniuses but run by idiots. The oil-and-gas trader used next-generation financial tools designed by brilliant mathematicians. But they couldn’t overcome the inept and criminal actions of the management.

The allure of a unifying, perfect mathematical formula is powerful; it is an alchemy for the enlightened age. Math’s universal principles underlie and suffuse everyday life and the workings of the cosmos, offering a glimpse of the eternal. In the frequently irrational financial markets, mathematic models offer the hope of cool reason and certitude, a sort of godlike wisdom.

In the 1998 film “Pi,” a troubled math genius who sees patterns in the newspaper stock tables tries to create the Algorithm for Everything. He and his work are simultaneously hunted by a Wall Street firm that seeks its predictive powers, and by orthodox Jews, who believe it could unlock the mind of God.

The quant funds thrive on volatility — it’s how they make their profit margins. But recent weeks have proved too volatile for some of the funds, many of them highly leveraged, which seemingly all at once got spooked into seeking liquidity. When they ended up seeking liquidity by selling the same stocks, the Aug. 9 plunge happened, analysts speculate, resulting in the Dow’s second-largest one-day slump of the year.

“It became increasingly transparent that many of the highly sophisticated quant funds employed similar investment approaches and held similar core holdings,” Thomson Financial wrote in an analysis of the role of the 25 largest quant funds in the market meltdown. “This resulted in the funds selling similar long stocks and covering similar short positions.”

For instance, the most broadly held stock among the top quant shops, Thomson reported, is Exxon Mobil. Shares of the oil company dropped 2.4 percent in heavy trading during the Aug. 9 sell-off.

“If you ask the question, ‘Did the smart guys blow it or get it right?’ I think the answer is, if they knew it, it wouldn’t have happened,” said David Levine, a vice president in corporate advisory services at Thomson.

“I occasionally hear broad statements like, ‘This just shows computer models don’t always work,’ ” Clifford S. Asness, founding principal of the quant-fund firm AQR Capital Management, wrote to his clients after the sell-off. “That’s true, of course, they don’t, nothing always works. However, this isn’t about models, this is about a strategy getting too crowded, as other successful strategies both quantitative and non-quantitative have gotten many times in the past, and then suffering when too many try to get out the same door.”

The value of Simons’s $29 billion Renaissance Institutional Equities Fund fell by nearly 9 percent from the beginning of the month through the Aug. 9 drop, Bloomberg News reported. It was less of a hit than many of the other quants took, possibly reinforcing Simons’s status as the Dumbledore of the quants.

A mathematician and cryptanalyst, Simons headed the math department of the State University of New York at Stony Brook, pushing the program into the nation’s elite.

Simons and his colleagues work in a form of high math decipherable to a handful of humans on the planet. As such, practitioners of the rare mathematic arts can become the powerful priests of investing, thanks to their strange and obscure language, much the way the medieval church trafficked in Latin, which required the translation of a learned cleric.

In 1978, Simons began to apply his predictive models to investing and set up his investment shop on the north shore of Long Island near his old school, virtually insulated from Manhattan’s financial district. He generally recruits mathematicians and programmers, not MBAs and traders.

The press-shy Simons would not comment for this article, and a Renaissance spokesman could not be reached for a comment.

The truth about men

Via NYTimes’ Tierny Lab blog, this address to the American Psychological Association.

What was the audience reaction? Did people run from the room to avoid vomiting at Baumeister’s horrible remarks? Do psychologists not have an intuitive understanding of variance? Why does Baumeister take so long to explain something so mathematically simple?

Former Harvard president Larry Summers’ vile comments here.

Is There Anything Good About Men?

R. Baumeister, Eppes Eminent Professor of Psychology & Head of Social Psychology Area, Florida State University

… I’m sure you’re expecting me to talk about Larry Summers at some point, so let’s get it over with! You recall, he was the president of Harvard. As summarized in The Economist, “Mr Summers infuriated the feminist establishment by wondering out loud whether the prejudice alone could explain the shortage of women at the top of science.” After initially saying, it’s possible that maybe there aren’t as many women physics professors at Harvard because there aren’t as many women as men with that high innate ability, just one possible explanation among others, he had to apologize, retract, promise huge sums of money, and not long afterward he resigned.

What was his crime? Nobody accused him of actually discriminating against women. His misdeed was to think thoughts that are not allowed to be thought, namely that there might be more men with high ability. The only permissible explanation for the lack of top women scientists is patriarchy — that men are conspiring to keep women down. It can’t be ability. Actually, there is some evidence that men on average are a little better at math, but let’s assume Summers was talking about general intelligence. People can point to plenty of data that the average IQ of adult men is about the same as the average for women. So to suggest that men are smarter than women is wrong. No wonder some women were offended.

But that’s not what he said. He said there were more men at the top levels of ability. That could still be true despite the average being the same — if there are also more men at the bottom of the distribution, more really stupid men than women. During the controversy about his remarks, I didn’t see anybody raise this question, but the data are there, indeed abundant, and they are indisputable. There are more males than females with really low IQs. Indeed, the pattern with mental retardation is the same as with genius, namely that as you go from mild to medium to extreme, the preponderance of males gets bigger.

All those retarded boys are not the handiwork of patriarchy. Men are not conspiring together to make each other’s sons mentally retarded.

Almost certainly, it is something biological and genetic. And my guess is that the greater proportion of men at both extremes of the IQ distribution is part of the same pattern. Nature rolls the dice with men more than women. Men go to extremes more than women. It’s true not just with IQ but also with other things, even height: The male distribution of height is flatter, with more really tall and really short men.
Again, there is a reason for this, to which I shall return.

Tierney’s summary:

“I’m certainly not denying that culture has exploited women,” he said. “But rather than seeing culture as patriarchy, which is to say a conspiracy by men to exploit women, I think it’s more accurate to understand culture (e.g., a country, a religion) as an abstract system that competes against rival systems — and that uses both men and women, often in different ways, to advance its cause.”

The “single most underappreciated fact about gender,” he said, is the ratio of our male to female ancestors. While it’s true that about half of all the people who ever lived were men, the typical male was much more likely than the typical woman to die without reproducing. Citing recent DNA research, Dr. Baumeister explained that today’s human population is descended from twice as many women as men. Maybe 80 percent of women reproduced, whereas only 40 percent of men did.

“It would be shocking if these vastly different reproductive odds for men and women failed to produce some personality differences,” he said, and continued:

For women throughout history (and prehistory), the odds of reproducing have been pretty good. Later in this talk we will ponder things like, why was it so rare for a hundred women to get together and build a ship and sail off to explore unknown regions, whereas men have fairly regularly done such things? But taking chances like that would be stupid, from the perspective of a biological organism seeking to reproduce. They might drown or be killed by savages or catch a disease. For women, the optimal thing to do is go along with the crowd, be nice, play it safe. The odds are good that men will come along and offer sex and you’ll be able to have babies. All that matters is choosing the best offer. We’re descended from women who played it safe.

For men, the outlook was radically different. If you go along with the crowd and play it safe, the odds are you won’t have children. Most men who ever lived did not have descendants who are alive today. Their lines were dead ends. Hence it was necessary to take chances, try new things, be creative, explore other possibilities.

The second big motivational difference between the genders, he went on, involves the kind of social relationships sought by each sex. While other researcher have argued that women are more “social” than men – more helpful and less aggressive towards others — Dr. Baumeister argued that women can be plenty aggressive in the relationships that matter most to them, which are intimate relationships. Men are more aggressive when it comes to dealing with strangers, because they’re more interested than women are in a wider network of shallow relationships.

“We shouldn’t automatically see men as second-class human beings simply because they specialize in the less important, less satisfying kind of relationship,” he said. Men are social, too, he said, just in a different way, with more focus on larger groups: “If you make a list of activities that are done in large groups, you are likely to have a list of things that men do and enjoy more than women: team sports, politics, large corporations, economic networks, and so forth.”