Strategic asset allocation and market timing: a reinforcement learning approach

  • Authors:

  • Thorsten Hens;Peter Wöhrmann

  • Affiliations:

  • Swiss Banking Institute, University of Zürich, Zürich, Switzerland 8032 and Norwegian School of Economics and Business Administration, Bergen, Norway 5045;Department of Management Science and Engineering, Stanford University, Stanford, USA

  • Venue:
  • Computational Economics
  • Year:
  • 2007

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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.