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


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  • Shu-Heng Chen
  • Chia-Hsuan Yeh

    (Department of Economics, National Chengchi University, Taiwan)


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. While search plays an important role in the EMH, tradtional notion does not formalize serach in a way such that it can be implemented, and it turns out that the EMH based on this notion is practically uncomputable. Compared with the traditional notion, a GP-based search provided 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 U.S. stock market. A short-term sample of TAIEX and S\&P 500 with the highest complexity defined 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 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 efficient market hypothesis can sustain from this perspective.

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Paper provided by University of California at Los Angeles, Center for Computable Economics in its series Working Papers with number _011.

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Handle: RePEc:wop:callce:_011

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  1. Francis X. Diebold & James M. Nason, 1989. "Nonparametric exchange rate prediction?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 81, Board of Governors of the Federal Reserve System (U.S.).
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  3. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July.
  4. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, Elsevier, vol. 9(4), pages 527-529, December.
  5. Hinich, Melvin J & Patterson, Douglas M, 1985. "Evidence of Nonlinearity in Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 3(1), pages 69-77, January.
  6. Frank, Murray & Gencay, Ramazan & Stengos, Thanasis, 1988. "International chaos?," European Economic Review, Elsevier, Elsevier, vol. 32(8), pages 1569-1584, October.
  7. repec:fth:guelph:1988-15 is not listed on IDEAS
  8. Willey, Thomas, 1992. "Testing for nonlinear dependence in daily stock indices," Journal of Economics and Business, Elsevier, Elsevier, vol. 44(1), pages 63-76, February.
  9. Savit, R., 1989. "Nonlinearities And Chaotic Effects In Options Prices," Papers, Columbia - Center for Futures Markets 184, Columbia - Center for Futures Markets.
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Cited by:
  1. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 31(10), pages 3396-3426, October.
  2. : Roman Kozhan & Mark Salmon, 2010. "The information Content of a Limit Order Book:the Case of an FX Market," Working Papers, Warwick Business School, Finance Group wpn10-05, Warwick Business School, Finance Group.
  3. Xue-Zhong He & Youwei Li, 2008. "Heterogeneity, convergence, and autocorrelations," Quantitative Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 8(1), pages 59-79.


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