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A Dynamic Asset Allocation Method based on Reinforcement learning Exploiting Local Traders  

O Jangmin (서울대학교 컴퓨터공학부)
Lee Jongwoo (숙명여자대학교 멀티미디어학과)
Zhang Byoung-Tak (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.8, 2005 , pp. 693-703 More about this Journal
Abstract
Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy 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.
Keywords
stock trading; asset allocation; reinforcement learning;
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