From a computation-theoretic standpoint, this paper formalizes the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalization differs from the traditional notion based on probabilistic independence in its treatment of search. Compared with the traditional notion, a GP-based search provides an explicit and efficient search program upon which an objective measure for predictability can be formalized in terms of search intensity and chance of success in the search. This will be illustrated by an example of applying GP to predict chaotic time series. Then, the EMH based on this notion will be exemplied by an application to the Taiwan and U.S. stock market. A short-term sample of TAIEX and S&P 500 with the highest complexity dened by Rissanen's MDLP (Minimum Description Length Principle) is chosen and tested. It is found that, while linear models cannot predict better than the random walk, a GP-based search can beat random walk by 50%. It therefore confirms the belief that while the shortterm nonlinear regularities might still exist, the search costs of discovering them might be too high to make the exploitation of these regularities protable, hence the efficient market hypothesis is sustained.
Journal of Ecnonomic Dynamics and Control,21(6),1043-1063