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Dynamic asset allocation exploiting predictors in reinforcement learning framework

Authors
O, JMLee, JWLee, JZhang, BT
Issue Date
Sep-2004
Publisher
SPRINGER-VERLAG BERLIN
Citation
MACHINE LEARNING: ECML 2004, PROCEEDINGS, v.3201, pp 298 - 309
Pages
12
Journal Title
MACHINE LEARNING: ECML 2004, PROCEEDINGS
Volume
3201
Start Page
298
End Page
309
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16120
ISSN
0302-9743
1611-3349
Abstract
Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset to be allocated to each recommendations of the predictors, we design an asset allocator called meta policy in the Q-learning framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed asset allocator outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.
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