Book Review: The Quants, Part II–The Meaning of Models, Statistics and Social Science

Patterson’s book is unduly modest; The Quants offers a marvelous satire about the nature of quantitative social science.  Though they are only fleetingly-mentioned, Nicholas Nassim Taleb’s “black swans” figure prominently here.  Patterson exhibits great sympathy for those quantitative skeptics of the Quants, prominently mathematician Benoit Mandelbrot, investor and professional doomsayer Nassim Nicholas Taleb, and Ed Thorpe, the first prominent Quant hedge fund manager.

Benoit Mandelbrot certainly isn’t as well-known as he should be; he is the economist who realized that normal distributions don’t really apply to quantitative data in economics.  While economist Louis Bachelier had already presented “Brownian motion” as the proper metaphor to describe likely shifts in stock prices, Mandelbrot used the longest-ranging data on stock prices available–prices on the New York Cotton Exchange since 1870–as the basis for an argument that fat-tailed distributions better-characterize much of quantitative data in social phenomena.

Brownian motion refers to the random movements of pollen grains or dust particles in undisturbed air.  While they wobble about aimlessly, buffeted this way and that by the air, when all else is equal they always wobble about a focal location.  Statistical arbitrage of stocks is based on the premise that stock prices rise and fall in a kind of Brownian motion, responses to contingencies that still have a common point of reference, a central mean to which they ultimately correct relative to certain other stocks.  From his study of NYCE prices and other long-running speculative data, Mandelbrot arrived at a different metaphor: The blindfolded archer.  A skilled archer may fire many shots that cluster within a close deviation of a target, but due to a twitch his 1,001st shot may fall wildly off the mark and even skip off the target; this one unforeseen event trivializes the 1,000 preceding normally-distributed shots.

Nassim Nicholas Taleb is probably known to the widest audience through his book The Black Swan, which he published in 2007 and promoted on The Colbert Report.  While he is one of the Quants by trade, he was a skeptic about the future of a financial sector that was unabashedly increasing uncertainty by increasing systemic complexity by speeding up its process of arbitraging stocks.  Much like the discovery of black swans in Austrailia by Dutch explorers, Taleb is obsessed by the thought that truisms only have to be repudiated once in experience to be found fraudulent.  Theoretically-incongruous “black swans”–the fall of the Soviet Union, September 11th, the rise of Google–often have a bigger impact on history than theories constructed to systematize old paradigms that turn out to be nothing more than moribund equilibria, soon to die and never to appear again.  Taleb believes little more can be done about these “black swans” than to develop a sense for one’s emergence, and to try to recognize the risks or opportunities involved.  For Taleb’s part, Patterson reported in the Wall Street Journal on November 3, 2008 (available to subscribers only) that the Quant investor and professional doomsayer had turned a 65% to 115% profit on investments made through his “Black Swan Protection Protocol.”

Ed Thorpe, the first of the Quant hedge fund managers, actually stopped trading in October 2002 because of a simple mathematical intuition:

“…So much money was flooding into the field that it was becoming impossible to put up solid returns without taking on too much risk.  Copycats were operating everywhere in a field he’d once dominated…” (p-151-52)

The popularity of hedge funds, statistical arbitrage of stock, and algorithms and computer trades that allowed the arbitrage to be made much more-quickly, had made the available gains of arbitrage so small that the only way to turn the old massive profits was to borrow more money and then use faster computers and updated algorithms to short-sell overpriced stocks and buy undervalued stocks even faster.  The sector had become saturated and refused to acknowledge it–and so committed to both more borrowing and higher risk.  Seen from this perspective, the wave of defaults, the massive sell-off of stocks, the housing glut and the deep debt of some investment banks was inevitable.  The new class of financiers, who prided themselves on their grasp of Alpha, were actually shamefully lacking in perspective.

All the pressure to take on more leverage to double-down investments, the reliance on computerized trading algorithms, the inability of mortgage-holders to pay and of the speculators on their debt to cover their credit-default swaps, constitutes a repudiation of the Efficient Market Hypothesis.  Pioneered by Eugene Fama, the Efficient Market Hypothesis maintains that stock prices rise and fall in more-or-less rational response to whatever information becomes available bearing on the proper value of that stock.  Whatever is known about a company is quickly disclosed through investors’ valuation of its stock.  The upshot of this premise is that there is no such thing as an investment bubble.

The problem with Eugene Fama’s “Efficient Market Hypothesis” is that it is wrong in a meaningful way; the boom-and-bust cycle itself demonstrates that investors in the mode do not and apparently cannot recognize an investment bubble when they see it, and so they invest in (or this time, actually facilitate) overproduction.  If frequent sales of credit-default swaps from owner to owner didn’t signal to many of the most-sophisticated hedge fund managers that investments grounded in the real estate and construction sector weren’t viable, Efficient Market Hypothesis is a myth that has been busted.  In the case of the real estate bubble, some of the investors invested in home production, encouraged more consumption by offering more mortgages and loans, and offered others collateralized debt obligations which usually paid higher dividends than stocks or bonds, but which meant that they would be answerable for other people’s credit defaults.  These sophisticated financial innovations facilitated a rapid run-up in financial assets, then brought the Dow Jones industrial average down to its mid-1990s level.  Those who worked out the details thought they had found Alpha, that they had a glimpse of “the Truth,” of an ebb-and-flow in financial markets.

In reality they were playing a sophisticated game of chicken, one that their rapid pricing arbitrage models and automated short-selling mechanisms actually accelerated.  Again, the Liberal Ironist doesn’t think that hedge funds, derivatives, or arbitrage algorithms are evil, but they, like statistical results in social science, are based on provisional truths that will be untrue someday.  In the meantime a “race” to increase the amount of detail the models can accommodate can result in intellectual shortcuts and a drive of investment practitioners away from “real world” economic conditions.

The Liberal Ironist doesn’t often find cause to refer to a “real world;” suffice to say that there were very real things the Quant hedge fund managers weren’t aware of until their own computers used their own trading models to fritter away their own assets.  Descartes recognized that, even if the external world itself weren’t real, the ego that was deceived about its existence must still be real; the Liberal Ironist will conclude, in disagreement with Eugene Fama, that the shock with which so many hedge fund managers received the stock market crises of late 2007 and late 2008 suggests that economic bubbles are very real, too.

Hedge funds can make real money, but they ought to be regulated by government; for related reasons, those interested in social or political theory can learn much from the findings of statistical social science research, but what they can learn from them often isn’t profound: These findings often refer to generational rather than enduring dynamics–and one must be sensitive to the diminishing returns of progressively-detailed research findings that sometimes pass for “progress.”  Political Science didn’t portend the fall of the Soviet Union or al-Qaeda’s attack on the United States; those great punctuations of equilibrium were called by historians and intelligence professionals, respectively.  Fundamentals are fundamental–and they are both contaminated and ephemeral.


4 thoughts on “Book Review: The Quants, Part II–The Meaning of Models, Statistics and Social Science

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