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Strategic asset allocation and market timing: a reinforcement learning approach


Woehrmann, Peter; Hens, Thorsten (2007). Strategic asset allocation and market timing: a reinforcement learning approach. Computational Economics, 29(3-4):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.

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Economics, Econometrics and Finance (miscellaneous)
Physical Sciences > Computer Science Applications
Language:English
Date:1 May 2007
Deposited On:17 Jul 2014 10:25
Last Modified:03 Feb 2024 08:15
Publisher:Springer
ISSN:0927-7099
OA Status:Green
Publisher DOI:https://doi.org/10.1007/s10614-006-9064-0
Official URL:http://link.springer.com/article/10.1007/s10614-006-9064-0
Other Identification Number:merlin-id:3559
  • Content: Published Version
  • Language: English
  • Description: Nationallizenz 142-005