Predicting Stock Returns with Genetic Programming: Do the Short-Term Nonlinear Regularities Exist?
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:95-101, 1995.
This paper is devoted to applying the genetic programming paradigm to the test of the captial market efficiency hypothesis. How this paradigm is distinguished from the existing statistical approaches is briefly reviewed. Instead of using the large-sample analysis prevailing in the literature, this research rests on a small-sample analysis to inquire the existence of short-term non-linear regularities. By Rissanen’s MDLP (Minimum Description Length Principle), the sample period with the highest complexity is chosen. Since our simulation results, which are based on Koza’s genetic programming paradigm (KGP) and its Bayesian modification (BGP), show that it is not easy to outperform $A R(1)$ and is extremely difficult to beat random walk, the nonlinear regularities, while might exist, is very difficult to be found. Therefore, the capital market efficiency hypothesis can, at least, sustain from this perspective.