Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trad- ing performance. To optimize the proportion of asset to be allocated to each recommendations of the predictors, we design an asset alloca- tor 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 ex- perimental 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.
['Jangmin O', 'Jae Won Lee', 'Jongwoo Lee', 'Byoung-Tak Zhang']
Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework