Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{chen:1996:caemh,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Toward a Computable Approach to the Efficient Market
                 Hypothesis: An Application of Genetic Programming",
  journal =      "Journal of Economic Dynamics and Control",
  year =         "1997",
  volume =       "21",
  number =       "6",
  pages =        "1043--1063",
  month =        "1 " # jun,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Minimum description length principle, Mean
                 absolute percentage error, Efficient market
                 hypothesis",
  DOI =          "doi:10.1016/S0165-1889(97)82991-0",
  URL =          "http://www.sciencedirect.com/science/article/B6V85-3SWYBJD-P/2/d1bb80ffce780c45697f44001e20f672",
  abstract =     "From a computation-theoretic standpoint, this paper
                 formalises 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 exemplified by an application to the
                 Taiwan and US stock market. A short-term sample of
                 TAIEX and S&P 500 with the highest complexity defined
                 by Rissanen's minimum description length principle
                 (MDLP) 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
                 short-term nonlinear regularities might still exist,
                 the search costs of discovering them might be too high
                 to make the exploitation of these regularities
                 profitable, hence the efficient market hypothesis is
                 sustained.",
  notes =        "Society of Computational Economics Conference JEL
                 classification codes: C63; G14",
}

Genetic Programming entries for Shu-Heng Chen Chia Hsuan Yeh

Citations