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MAY 20, 2009


Machines versus Humans

How computer modeling worsened the financial crisis and what we ought to do about it

Machine is never better than human. The machines help for the analytic ordering of information collected by humans and/or machines, they are an irreplaceable assistant to human work, but they can never be enough good as tools for prognostics of complex social systems such as the market - a highly unpredictable system based on human activities, emotions and needs.

According to Joseph Fuller - co-founder of the global consulting firm Monitor this fact has been forgotten by the majority of stock traders over the world in recent years. "Over the past 20 years on Wall Street, says Fuller in an article called "The Terminator Comes to Wall Street" (The American Scholar), 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."

Using either mathematical or statistical models, Fuller notes, firms have been able to trade huge volumes of securities globally. In September 2008 the global stock exchange NYSE Euronext reported that the so-called "program trading," in which computers execute trades based on programs developed by Quants without specific human intervention, represents almost 17 percent of trades - or more than 900 million shares per day.

Joseph Fuller detects three inherent problems in the present shift from human management to machine based analysis. The first problem is the character of market and the education of the men who create the Quant models. "Modelers are experts in math, computer science, or physics. They are not generally experts in stocks, bonds, markets, or psychology," says Fuller. The machines they create work whithin parameters that usually do not include "unprecedented circumstances" such as financial crises or natural disasters.

The second problem is that managers very often don't understand the modelers and the Quants (the programs) on which they rely. They are not able to understand the sophisticated formulas and parameters lying behind the end data which they see on their computer screens. The lack of strong connection and understanding between decision makers and the very source of information and analysis on which they rely makes their actions extremely risky.

The third problem, writes Fuller, is that the models itself 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."

Joseph Fuller, who is a regular contributor of The Wall Street Journal, the Financial Times, and Harvard Business Review, explains the current financial crisis partly with the overreliance on machine generated models in the last decades.

Fuller's conclusion is that "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."

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