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Strategic asset allocation and market timing: a reinforcement learning approach

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Author Info

  • Thorsten Hens

    ()

  • Peter Wöhrmann

    ()

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    Abstract

    We apply the recurrent reinforcement learning method of Moody, Wu, Liao, and Saffell (1998) in the context of the strategic asset allocation computed for sample data from US, UK, Germany, and Japan. It is found that the optimal asset allocation deviates substantially from the fixed-mix rule. The investor actively times the market and he is able to outperform it consistently over the almost two decades we analyze. Copyright Springer Science+Business Media, LLC 2007

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    File URL: http://hdl.handle.net/10.1007/s10614-006-9064-0
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    Bibliographic Info

    Article provided by Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 29 (2007)
    Issue (Month): 3 (May)
    Pages: 369-381

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    Handle: RePEc:kap:compec:v:29:y:2007:i:3:p:369-381

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    Web page: http://www.springerlink.com/link.asp?id=100248
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    Related research

    Keywords: Dynamic asset allocation; Bond/equity ratio; Reinforcement Learning;

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    8. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    9. De Bondt, Werner F M & Thaler, Richard, 1985. " Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    10. Samuelson, Paul A, 1969. "Lifetime Portfolio Selection by Dynamic Stochastic Programming," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 239-46, August.
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