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Alan Greenspan’s Love Affair with Technology

7/20/2010

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Throughout his autobiography, The Age of Turbulence, Alan Greenspan expresses a deep fascination for the ways in which technology has transformed our economy. Among other changes, technology has revolutionized the distribution of risk, he maintains, and has also increased the ability of the markets to absorb shocks.  As a result, the economy has a new and--a very modern—flexibility. “Three or four decades ago, markets could deal only with plain vanilla stocks and bonds. Financial derivatives were simple and few,” Greenspan writes. “But with the advent of the ability to do around-the-clock business real-time in today’s linked worldwide markets, derivatives, collateralized debt obligations, and other complex products have arisen that can distribute risk across financial products, geography, and time.  . . . With the exceptions of financial spasms [as in 1987 and 1998], . . . markets seem to adjust smoothly from one hour to the next, one day to the next, as if guided by an ‘international invisible hand,’ if I may paraphrase Adam Smith.” Driven by advanced technology, the modern market process improves market efficiency and hence raises productivity. It is a triumph of modern information technologies.

Greenspan’s habit of mind made his enthusiasm for information technology inevitable. The long-time Chairman of the Federal Reserve never met a number he didn’t love. In his lifelong search for new knowledge and insights about the economy, he liked nothing better than absorbing large quantities of economic data. Lack of emotional bias was central to his search.  In his twenties he was attracted to logical positivism, a school of thought popular with Manhattan Project scientists. According to logical positivism, knowledge could only be obtained from facts and numbers. Values, ethics, personal behavior were not logical in nature. Rather they were shaped by the dominant culture and hence not past of serious thinking on any subject.  Greenspan would later emend this view, particularly regarding values, but the idea that facts and numbers were the path to knowledge remained part of his core beliefs.

A course he took in 1951 in mathematical statistics provided him with a scientific basis for his beliefs. Mathematical statistics proposed that the economy can be measured, modeled, and analyzed mathematically. (It was a nascent form of what is known today as econometrics.) Greenspan was immediately attracted to this discipline—and he excelled in it. Here was a forecasting method based on mathematics and empirical facts. Many prominent economists at the time, Greenspan observed, relied on “quasi-scientific intuition” in their forecasting, but he himself was inclined to develop his thinking in a different way: “My early training was to immerse myself in extensive detail in the workings of some small part of the world and infer from that detail the way that segment of the world behaves.  That is the process I have applied throughout my career.”

Little wonder that when digital computers began to invade the business world, Greenspan naturally saw them as extremely effective in gathering and ordering vast amounts of data and numbers: It is after all what computers do best. In fact, the span of Greenspan’s career did coincide with a revolution in financial markets based on digital computers.  He like many others saw in the innovations and improved efficiency that technology brought to the markets great progress. As long as technology was contributing to productivity growth and to general wealth, he could see nothing wrong with it. In fact, he often makes assumptions and even illogical arguments all in the name of technological progress. One example involves his attitude toward increased debt levels for both individuals and businesses. Yes, he admits there has been a long-term increase in leverage. But the appropriate level of leverage is a relative value that varies over time. Greenspan further minimizes the ramifications of increased leverage by arguing that people are steadfastly and innately averse to risk. Technology has simply added more flexibility in the system. Thus, Greenspan concludes, the general willingness of investors, businesses, and households to take on more leverage must mean that the additional financial “flexibility” allows for increased leverage without increased risk. “Rising leverage,” Greenspan blithely concluded, ”appears to be the result of massive improvements in technology and infrastructure, not significantly more risk-inclined humans.”

In the end, Greenspan was forced to change his mind about technology after the recent financial crisis. In his testimony to Congress in April of this year, he found two major ways in which technology had indeed failed the markets and helped precipitate the crisis. First of all the models that sophisticated investors used to assess risks were wrong. Those models had no relevant data that would have allowed them to forecast the impact of an event such as the failure of Lehmann Brothers. Investors and analysts had relied on pure—and incorrect—conjecture: They decided that they would be able to anticipate such a catastrophic event and retrench to avoid exposure. They were wrong.

Secondly, financial models for assessing risks, combined with huge computational capacities to create highly complex financial products, had left most of the investment community in the dark. They didn’t understand the products or the risks involved. Their only option was to rely on the rating agencies, which were in effect no better at assessing the risks of these products than anyone else.  Technology and those brilliant Ph. D.s known as the “quants” had effectively created their own monsters in the form of credit default swaps and collateralized debt obligations, which were far too opaque for even sophisticated investors to understand.

So much for technological innovations and flexibility. In the end it was in part technology that set up the conditions for the worst economic crisis since the Depression. One is left to wonder where that “international invisible hand” has gone to now.
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Machines in the Markets: Automating Herd Behavior

7/1/2010

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According to the Wall Street Journal, programmed trading is expected to account for 60% of all trades this year, up from just 28% in 2005. Yet the very programs that supposedly guarantee some rationality and repeatability in the markets can actually add to instability overall.

Financial models have been severely and widely criticized in the wake of the recent financial crisis. Many, including Pablo Triana in Lecturing Birds on Flying (2009), echo Nassim Taleb’s Black Swan arguments about the importance of rare events in financial markets. Financial models are faulty because they use historical data—often just the previous two years or so—to predict the future. They also produce errors because they use normal probability distribution, that is, they assign negligible odds to the possibility that asset prices will swing wildly. Oddly enough, however, the very use of automated programs can itself increase volatility, creating a downward spiral that feeds upon itself . Economists have long understood the uncertainties inherent in markets because of human behavior. Now it seems there is another significant source of uncertainty built into markets as more and more computer programs are used to manage assets.

In computer-driven asset management, trading companies write computer algorithms that dictate the selection of investments and positions in the markets. Advocates for this sort of automation like to point out that computers have several advantages over human beings when it comes to selecting and managing assets. For one thing, a computer algorithm takes the emotion out of picking stocks and deciding when to buy and sell. It follows clear rules (albeit written by humans) that eliminate the risks of irrational exuberance. There is no fondness for a certain company or industry sector that can bias decisions, nor is there any tendency toward heightened fear in the face of uncertainties. Repeatability and rationality do appear to rule.

Computers can also work around the clock seven days a week without losing judgment or falling asleep. And, in a world where massive amounts of information are being generated both rapidly and constantly, computers are capable of handling much more data than human beings can. In short, computers would seem to be the rational antithesis of the kind of messy and unpredictable human behavior that colors traditional decision-making on and off the trading floor.  

Oddly enough it is the very success of these quantitative trading tools that creates the potential for increasing instability into the markets. As Pablo Triana points out, when one company is highly successful using a particular algorithm, other Wall Street firms are quick to imitate that success, incorporating similar schemes in their own models. It’s much easier, Triana goes on to say, to copy  another company’s program for trading in great detail than it is, for example, to copy the hunches and insights of traditional trading methods: “Precise mathematical concoctions are much more amenable to exact replication and copying than human emotions.”

This kind of easy replication results in two kinds of trading patterns. The first is that all the professional traders end up holding very similar positions. The second is that they can all start selling off at the same time, an event that can trigger a more general sell-off in the market. This is what occurred in August of 2007. And it seems to have contributed to the downdraft, the “flash crash,” of May 6th of this year. It’s a growing cause for concern because algorithmic trading is rising rapidly. According to the Wall Street Journal, programmed trading is expected to account for 60% of all trades this year, up from just 28% in 2005 ( “FastTraders Face Off with Big Investors over ‘Gaming,’” http://online.wsj.com/article/SB10001424052748703374104575337270344199734.html?mod=WSJ_Markets_LeadStory .)
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