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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Strategic asset allocation and market timing: a reinforcement learning approach |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computational Economics |
| Publisher | Springer |
| Geographical Reach | international |
| ISSN | 0927-7099 |
| Volume | 29 |
| Number | 3-4 |
| Page Range | 369 - 381 |
| Date | 2007 |
| Abstract Text | 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. |
| Official URL | http://link.springer.com/article/10.1007/s10614-006-9064-0 |
| Digital Object Identifier | 10.1007/s10614-006-9064-0 |
| Other Identification Number | merlin-id:3559 |
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