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 backdrop, 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 program developed by Quants without specific human intervention, represented almost 17 percent of trades – more than 900 million shares per day.

 

Since the data that feed these analytical formulas come from the past, the models can have trouble responding to extraordinary or unprecedented events.  When credit markets began to seize up in mid-2008 and the securities markets went into free fall, the models tried to figure out a suitable response.  They had been programmed to avoid volatility by moving out of securities and into cash.  Of course, when many models trading hundreds of millions of shares all tried to liquidate investments and move into cash, they only increased the stock declines, leading to further volatility and thus to more selling.  The models had not been programmed to understand a scenario in which everyone might try to move to cash at the same time.  The effect was like a panicked crowd trying to escape from a burning theatre...

 

 

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.  ...[T]his 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...

 

Investment firms will wrangle with the challenge of tying their models to a better understanding of market behaviors.  Regulators will struggle to adjust market rules to curtail the explosive effects of existing models.  Meanwhile, new types of computer-based trading programs will emerge as technologies driving them continue to evolve.  At the cutting edge of modeling science, researchers are trying to move away from relatively crude rules-based models toward models that approximate the processes of human reason...

 

With more oversight and better management at the investment firms, and more intelligent regulation, it’s possible to create an environment in which the Quants and their programs enable liquidity and productivity, with reduced volatility.  We don’t need any more real-life technology-gone-wrong scenarios.