Dynamic asset allocation exploiting predictors in reinforcement learning framework

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.