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Strategic asset allocation and market timing: a reinforcement learning approach

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  • Thorsten Hens
  • Peter Wöhrmann

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

Suggested Citation

  • Thorsten Hens & Peter Wöhrmann, 2007. "Strategic asset allocation and market timing: a reinforcement learning approach," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 369-381, May.
    • Handle: RePEc:kap:compec:v:29:y:2007:i:3:p:369-381
    DOI: 10.1007/s10614-006-9064-0
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    Cited by:

    1. Jin-Ray Lu & Chih-Ming Chan & Wen-Shen Li, 2011. "Portfolio Selections with Innate Learning Ability," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 10(3), pages 201-217, December.

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