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Limited profit in predictable stock markets

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  • Rothenstein, Roland
  • Pawelzik, Klaus

Abstract

It has been assumed that arbitrage profits are not possible in efficient markets, because future prices are not predictable. Here, we show that predictability alone is not a sufficient measure of market efficiency because of the influence an order has on its dynamics. We instead propose to measure inefficiencies of markets in terms of the maximal profit an ideal trader who can perfectly predict the future behavior of the market can take out from a market. In a stock market model with an evolutionary selection of agents this method reveals that, the mean relative amount of realizable profits P is very limited and we find that it decays with the rising number of agents. Our results show that markets may self-organize their collective dynamics such that it becomes very sensitive to profit attacks, which demonstrates that a high degree of market efficiency can coexist with predictability.

Suggested Citation

  • Rothenstein, Roland & Pawelzik, Klaus, 2005. "Limited profit in predictable stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 419-427.
    • Handle: RePEc:eee:phsmap:v:348:y:2005:i:c:p:419-427
    DOI: 10.1016/j.physa.2004.09.010
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    References listed on IDEAS

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    1. Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2003. "Predicting the Future," Information Systems Frontiers, Springer, vol. 5(1), pages 47-61, January.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. G. Caldarelli & M. Marsili & Y. -C. Zhang, 1997. "A Prototype Model of Stock Exchange," Papers cond-mat/9709118, arXiv.org.
    4. J. Doyne Farmer & Andrew W. Lo, 1999. "Frontiers of Finance: Evolution and Efficient Markets," Working Papers 99-06-039, Santa Fe Institute.
    5. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    6. Levy, Haim & Levy, Moshe & Solomon, Sorin, 2000. "Microscopic Simulation of Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780124458901.
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    Cited by:

    1. Roland Rothenstein, 2018. "Quantification of market efficiency based on informational-entropy," Papers 1812.02371, arXiv.org.
    2. Paweł Fiedor, 2015. "Multiscale Analysis of the Predictability of Stock Returns," Risks, MDPI, vol. 3(2), pages 1-15, June.

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