Frontiers of Finance: Evolution and E cient Markets

In this review article, we explore several recent advances in the quantitative modeling of nancial markets. We begin with the E cient Markets Hypothesis and describe how this controversial idea has stimulated a number of new directions of research, some focusing on more elaborate mathematical models that are capable of rationalizing the empirical facts, others taking a completely di erent tack in rejecting rationality altogether. One of the most promising directions is to view nancial markets from a biological perspective and, speci cally, within an evolutionary framework in which markets, instruments, institutions, and investors interact and evolve dynamically according to the \law" of economic selection. Under this view, nancial agents compete and adapt, but they do not necessarily do so in an optimal fashion. Evolutionary and ecological models of nancial markets is truly a new frontier whose exploration has just begun. This research was partially supported by the MIT Laboratory for Financial Engineering and the National Science Foundation (Grant No. SBR{9709976). We thank the participants of the National Academy of Sciences 1998 Frontiers of Science Symposium for valuable discussions and comments. Prediction Company, 236 Montezuma Ave., Santa Fe NM, 87501, [email protected] (email). MIT Sloan School of Management, 50 Memorial Drive, E52-432, Cambridge, MA 02142{1347, (617) 253{0920 (voice), (617) 258{5727 (fax), [email protected] (email). If, in January 1926, an individual invested $1 in one-month US Treasury bills|one of the safest securities in the world|and continued reinvesting the proceeds month by month until December 1996, the original investment would have grown to $14. If, on the other hand, an individual invested $1 in the S&P 500|a much riskier investment|over the same 71-year period this investment would have grown to $1,370, a considerably larger sum. Now suppose that each month, an individual were able to divine in advance which of these two investments would yield a higher return for that month, and took advantage of this information by switching the running total of his initial $1 investment into the higher-yielding asset. What would a $1 investment in such a \perfect foresight" investment strategy become by December 1996? The startling answer is $2,296,183,456, more than two billion dollars! Despite the fact that perfect foresight in nancial markets is impossible, this example suggests that even a modest ability to forecast nancial asset returns may be handsomely rewarded. Of course, there are considerable risks involved. As recent nancial calamities such as that of Long Term Capital Management have made very clear, it is also possible to lose very large sums of money. Financial markets are very di cult to predict; otherwise we would all be rich. One of the central questions, indeed perhaps the most central question in nance, is under what circumstances is prediction possible at all? In this review article, we explore this issue in light of recent advances in the quantitative modeling of nancial markets. The potent combination of breakthroughs in nancial technology and computational speed and e ciency is creating an exciting renaissance in nancial research, both inside and outside the halls of academia. It is impossible for us to provide a complete survey of these developments here; instead, we focus on the beginnings of a new research direction that the emerging elds of computational nance and nancial engineering may be heading towards|evolutionary and ecological models of nancial markets|and how these new perspectives may be changing fundamental views about market prediction. Our starting point is the \E cient Markets Hypothesis" (EMH), a powerful idea that can be traced back to Paul Samuelson (1965), whose contribution is neatly summarized by the title of his article: \Proof that Properly Anticipated Prices Fluctuate Randomly". In an informationally e cient market, price changes must be unforecastable if they are properly anticipated, i.e., if they fully incorporate the expectations and information of all market