Equity markets and computational intelligence

I propose a new characterization of the types of problems for which computational intelligence (CI) tends to be used, namely the identification of approximate abstractions. I then suggest that equity markets provide a challenging example for CI. Because markets are inherently adaptive, they pose a more difficult problem than traditional CI domains. I discuss my experience teaching a CI class that took the development of stock trading systems as a theme. A simple genetic algorithm to generate a trading strategy was developed as a class example. Although the astonishingly good results it achieved were due at least in part to data snooping, a simple unevolved version of the same strategy was almost as profitable. Yet it too had subtle data snooping problems-showing how difficult it is to avoid data snooping entirely, especially in adaptive domains.