Bridging the gap between nonlinearity tests and the efficient market hypothesis by genetic programming

Abstract

Applies the genetic programming (GP) based notion of unpredictability to the testing of the efficient market hypothesis (EMH). This paper extends the study of Chen and Yeh (1995) by testing the EMH with a small, medium and large sample of the S&P 500 stock index. It is found that, in terms of the prediction performance, the probability /spl pi//sub 2/(n) that GP can beat the random walk tends to have a negative relation to the size of the in-sample dataset. For example, when the sample size n is 50, 200 and 2000, then /spl pi//sub 2/(n) is 0.5, 0.2 and 0, respectively. This therefore suggests that, while nonlinear regularities could exist, they might exist in a very short span. As a consequence, the search costs of discovering them might be too high to make the exploitation of these regularities profitable; hence, the EMH is sustained.