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The Terminator Comes to Wall Street: How computer modeling worsened the financial crisis

The American Scholar:

You’ve seen this story in countless Hollywood science-fiction movies, from The Terminator to War Games. Scientists develop a sophisticated computer or robot to assure the nation’s security, but something goes wrong and the technology itself mutates into a catastrophic threat. Unfortunately, the U.S. economic system now finds itself crippled by a real-life technology-gone-wrong story line. In this case, the culprit is not a Pentagon fighting machine, but rather the computer-based modeling and trading programs developed for Wall Street over the last quarter century.

Business models—whether they are models for analyzing market trends or running a major auto manufacturer—typically assume that history provides a guide to future outcomes. Such an assumption is usually reliable, but whenever events fall outside historical norms, the results can be catastrophic. Against this background, consider the introduction of computer-based program trading, arguably the most important change in global investing since the founding of the first mutual fund—the Massachusetts Investors Trust—in 1924. Over the past 20 years on Wall Street, computer-based models have gradually replaced human networks of strategists and traders. Quantitative analysts (“Quants”) trained in mathematics and physics have used sophisticated data analytics and modeling skills to evaluate securities and develop portfolio-management theories. The advent of Quants has allowed firms of all stripes to trade ever-larger volumes of securities and to extend their activities to new and exotic instruments. Using either mathematical or statistical models, firms have also been able to trade huge volumes of securities globally. In many cases, the computers didn’t just provide advice, they actually executed stock trades. By the end of September 2008, the global stock exchange NYSE Euronext reported that so-called program trading, in which computers execute trades based on programs developed by Quants without specific human intervention, represented almost 17 percent of trades—more than 900 million shares per day…

Computer models have three inherent problems. The first problem is that those who created the models don’t understand the markets. Modelers are experts in math, computer science, or physics. They are not generally experts in stocks, bonds, markets, or psychology. Modelers like to think of markets as efficient abstractions, but these abstractions can never fully account for the messy and irrational actions that humans take for emotional reasons. Moreover, as we have seen, they construct their models or programs based on a study of historical market data. They test them by showing how well the model would have performed in a given historical situation. Because their programs must have some parameters, modelers necessarily have to exclude unprecedented circumstances like the current simultaneous volatility in global debt, equity, currency, and commodity markets.

The second problem is that managers don’t understand the modelers. Most of the current generation of senior executives on Wall Street lack the technical background to understand the models (or the algorithms that underlie them) that power their own firms’ trading strategies. Because they are unable to speak the same language as the people creating the models, the managers have difficulty framing the questions necessary to comprehend how the models might respond to different situations. The problem here goes beyond comprehension. Even if the executives were Quants, they might well not understand as much as they would like about the programs running their businesses. The models themselves—and particularly the interaction among models—has grown so complex that it may have become impossible for any human to fully grasp the types and volumes of derivatives traded in this way or to predict how the models will interact with each other.

The third problem is that the models don’t “understand” each other. Each model executes its own strategy based on its calculus for maximizing value in a given market. But individual models are not able to take into account the role other models play in driving the markets. As a result, each program reacts almost in real time to the actions of other programs, potentially compounding volatility and leading to wild market swings. As we have seen, this happened recently when a set of models analyzing market data led their respective firms to liquidate assets and maximize their cash positions. The cumulative effect intensified the resulting selloff.

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