Bridging the gap between nonlinearity tests and the efficientmarket hypothesis by genetic programming
Shu-Heng Chen
Chia-Hsuan Yeh
AI-ECON Res. Group, Nat. Cheng Kung Univ., Tainan ;
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 π2(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 π2
(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
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