Scott Patterson‘s The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It is recommended reading for anyone curious as to how we got to where we are now–that is, how the Dow Jones Industrials average went from an October 12, 2007 weekly high of 14,093.08 to a March 6, 2009 weekly low of 6,626.09, why the official US unemployment rate was 9.6% this August, and why the annualized rate of home sales currently hovers around 4 million, the weakest sales volume since considerably less-housed 1997.
Patterson focuses his account on the academic mathematicians who moved to Wall Street to work on hedge funds. Many were highly-theoretical academics, not day traders or even Economics majors. Patterson raises the Quants’ reverent invocation of “Alpha,” the idea that, in the contingency of numbers they live by, the Quants can recognize an underlying rational order in the markets. Remember my earlier blog post on the fine-structure constant? The fine-structure constant, designated as “alpha,” is a proportion among the value of several other constants which serves as a numerical value for the strength of the various properties of nuclear physics, electromagnetism and radiation. It represents the relation between the micro-level forces of the Universe. For the Quant hedge fund traders, Alpha represented the chief intellectual accomplishment, the ability to grasp market movements almost through mathematical intuition, like the protagonist in the Darren Aronofsky thriller Pi.
The key to the way hedge funds turn their profits is statistical arbitrage. Unlike traditional investment, where one offers capital to a corporation based on an assessment of a company’s or an industry’s “fundamentals,” statistical arbitrage is based on the ability to “buy low and sell high”–as frequently as possible, without even waiting for information about conditions within the company or the industry. Arbitrage itself isn’t complicated (though the manner in which the most-dynamic hedge funds perform it today is literally inhumanly complex):
“Suppose GM typically traded for $10 and Ford for $5. A large buy order for GM could cause the price to rise temporarily to $10.50. Ford, meanwhile, would stay at $5. The ‘spread’ between the stocks had widened.” (p-42)
The point in statistical arbitrage is for the hedger (or the hedge fund) to sell its GM stock and use the dividends to double-down on Ford stock. This is done on the assumption, based on past probability, of either GM stock falling back down to $10 or Ford stock rising to $5.25 in maintenance of the traditional spread. While a crude, almost thoughtless means of buying and selling stock, the rough maintenance of these spreads in the value of stocks within an industry on average allowed hedge funds to generate billions of dollars in profits on buys and short sells–and allowed them to make massive returns on investments–for a while–the more-quickly they were able to buy and sell to level the change in stock prices.
While this probably sounds completely crazy, there is a real beauty to it: Since the ability of these hedge funds to buy a stock low and sell it high–many, many times a day–increases the prospects of huge returns for rich investors, this facilitates more investment, which gives corporations the capital they need to stimulate economic growth. Both the investors contributing capital to hedge funds and, in a sense, the hedge fund managers and arbitrage modelers themselves, literally had no idea why they were making money; they simply made a winning bet on which stocks are under- or overvalued. If this statistical hunch is ordinarily validated, then a bigger profit is contingent on making these trades more-quickly.
They were vindicated for years, and the Dow Jones Industrials Average ran up from 2,566.09 on January 4, 1991, the year Pete Muller opened a quantitative hedge fund at Morgan Stanley in New York and Ken Griffin was finishing his first year with his Citadel Investment Group in Chicago, to the aforementioned high of 14,093.08 on October 12, 2007. Today, nearly 3 years after that all-time high, the Dow Jones Industrials Average closed at 10,948.58. Preventing a free-fall of collapsing banks and drying credit markets required a massive repudiation in practice of market finance. Patterson’s book focuses on 4 hedge fund managers whose dogmatism (or naivete, if you prefer) first made them billionaires, then completely unmade their monetary contributions to the financial sector.
The hedge fund sector certainly was big-enough to destroy the economy. Hedge funds managed $2.5 trillion dollars as of late 2008, according to the Alternative Investment Management Association‘s “Road Map to Hedge Funds.” Considering the US gross domestic product was about $14.2 trillion in 2008, investments on that scale are clearly large-enough to tank the economy if they either create or overreact to investment dysfunction. Quant hedge funds did both, encouraging industries to overproduce desired goods (in this case housing) offering leverage to more people who needed it through their banking operations, and ultimately, going deeply into debt themselves in order to invest more money in their cycle of short-selling and derivatives-trading.
The succession of events isn’t complicated, even if the details are: The largest hedge funds fostered investment in a glut of new home construction through the innovation and rapid distribution of credit default swaps on subprime mortgages. Credit-default swaps are literally sold liabilities to pay down sub-prime mortgages in the event that the home buyers on the hook default; hedge funds would buy these credit default swaps, because while they held them, they actually paid off dividends better than a stock in the investor-saturated market. (Obviously, no one would buy a debt obligation for someone they don’t even know unless it paid a regular dividend which they found more-compelling than the risk of having to cover that person’s credit default.) Since banks considered these credit-default swaps to be “mortgage insurance” if homebuyers defaulted on their payments, they promptly lowered their standards for whom was eligible for a sub-prime mortgage! The jaw-dropping cover story for the Atlantic from last December revealed one of the ways in which a seemingly-independent part of our culture (Pentecostal preachers in megachurches) was colonized and its values reified by this growing shell-game: Pentecostal ministers of the “Prosperity Gospel” subsect literally encouraged congregants to buy houses and taken on mortgages on faith of being able to make payments, claiming that this was a demonstration of one’s faith rather than tempting the Lord. (In one of the most-damning passages of this article, the author pointed out that some Pentecostal ministers actually became loan officers for the large banks offering the mortgages in this period.) The banks offered more credit default swaps on the new mortgages, often bundled together and sold to hedge funds, those masters of the short sell…which meant that these hedge funds became liable for a vast aggregation of mortgages that literally didn’t have their own bond rating…which eventually prompted banks to sell credit default swaps on the bundles of credit default swaps…which would then be purchased by hedge funds that intended to short-sell them when their value increased, some of them operated by the same banks.
Laissez-faireists would play up the role of a Federal Reserve that kept interest rates low, but higher interest rates would only have further facilitated what became a key instrument of dangerous borrowing by the hedge funds: the carry trade. This was the borrowing of Yen at 0% (!) interest from Japanese banks and its conversion into US Dollars to catalyze this process of subprime mortgaging, selling of credit default swaps on risky mortgages (some of which were bought by hedge funds with cash borrowed from Japan), banks giving out more capital–no, leverage–on riskier sub-prime loans, and offering more credit default swaps. As Wall Street began to shudder under a succession of defaults, the Yen started appreciating against the US Dollar, which meant that to clear their balance sheets, the hedge funds that had blown the money they borrowed from Japanese banks would now have to come up with more money than they borrowed in the first place to pay off this debt. Financial liberalization likely played a bigger role in our recent economic collapse than the maintenance of a low interest rate. In Congressional testimony, then-Federal Reserve Chairman Alan Greenspan had repeatedly advised legislators against the regulation of hedge funds, assuring them of the growth potential for innovation of complex forms of financial arbitrage.
Hedge funds and credit default swaps are the innovations of brilliant mathematicians that big banks and hedge funds gave free reign. These innovations aren’t inherently-evil; the problem is their complex interactions and lack of regulation of their evolution and movement. If the characteristic weakness of government is inefficiency, the characteristic weakness of markets is living with uncertainty–even real ignorance–about opportunities and risks for investment. To leave it to the market to police its own imprudence (or in the case of banks that offer mortgages and profiting from offering credit-default swaps on those mortgages when they expect them to fail, fraud) overlooks the simple fact that only the entity which has the power to tax businesses, adjust monetary policy and fine or jail corporate criminals has the power to enforce compliance with legal principle and investigate defectors. The Liberal Ironist thinks that a myopic focus on either market efficiencies or “protecting the public” through regulation has led to absurdities; the trade-off is a legitimate subject of debate in politics.
Scott Patterson’s book is fascinating as a history–by turns both sympathetic and damning–of the very-academic “financial engineers” who recognized that mathematics could be used to profit from risk. While I was reading it occurred to me that the problems with formal models and statistical inferences on display in this book actually offer a legitimate cautionary tale for social science in general. But that’s a subject that warrants its own–immediately forthcoming–post.