Strategic asset allocation and market timing: a reinforcement learning approach

Thorsten Hens () and Peter Wöhrmann ()

Computational Economics, 2007, vol. 29, issue 3, 369-381

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

Keywords: Dynamic asset allocation; Bond/equity ratio; Reinforcement Learning (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s10614-006-9064-0

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