The efficient market hypothesis is, above all else, convenient. From a distance, it at once explains the overall behavior of equities markets, relieves the layperson of any obligation to become a student of finance, and provides the basis for the index fund, a fine financial instrument for the casual investor if there ever was one. But upon further inspection, strong-form EMH is desolate; it promises that you cannot do better no matter how hard you try. It is arrogant in its explanation of why one person does better than another person -- the explanation is always luck and in the long run you'll both end up in the same place.

Thus, the hopeful water down the strong-form efficient market hypothesis. Weak-form EMH allows for equities to be improperly assessed and priced by the market and the shrewd investor can thus produce excess returns. It's a rosier picture of the financial world, and one that suggests he who thinks the hardest can become the King of Investors.

What would the King of Investors look like? Would it be one guy with a really huge brain? Would it be a team of well-trained analysts? Would it be the guy on the Investools commercial who just told you he can make money in any market, whether it's going up, down, or sideways? Given the mass of stocks (there are thousands) and the considerable volume of data that comes with each stock, you can bet that the King of Investors will be a computer-aided endeavor, though Investools probably will not be involved.

The limits to what a computer and some good software can do are relatively unknown at this point. Much of the work in computational finance is inherently flawed -- preoccupied with technical analysis or conducted by teams lacking in one of the necessary disciplines (finance, information technology, statistics). Precious little unleashes the full brunt of the computer science arsenal (information retrieval, data mining, machine learning, etc) on the problem.

My goal is to seek the boundaries of the efficient market hypothesis. I'd tell you all about it but I do believe in weak-form EMH.

• Victor Lavrenko, Matthew D. Schmill, Dawn Lawrie, Paul Ogilvie, David Jensen and James Allan. "Language Models for Financial News Recommendation," In Proceedings of the Ninth International Conference on Information and Knowledge Management CIKM, Washington, DC November 6-11, 2000.
• Victor Lavrenko, Matthew D. Schmill, Dawn Lawrie, Paul Ogilvie, David Jensen and James Allan. "Mining of Concurent Text and Time Series". In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 37-44, 2000.