Strategic asset allocation and market timing: a reinforcement learning approach

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.

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Correspondence to Thorsten Hens.

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Hens, T., Wöhrmann, P. Strategic asset allocation and market timing: a reinforcement learning approach. Comput Econ 29, 369–381 (2007). https://doi.org/10.1007/s10614-006-9064-0

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Keywords

  • Dynamic asset allocation
  • Bond/equity ratio
  • Reinforcement Learning