Reward Adjustment Reinforcement Learning for Risk-averse Asset Allocation

Over the past decade, application of reinforcement learning (RL) in asset allocation and portfolio management has attracted much attention. However, most classical RL algorithms do not take risk into account, which may lead to treacherous trading decisions. In this paper, we propose a risk-averse RL method, named reward adjustment reinforcement learning. Our method incorporates risk to the classical RL framework by adjusting the reward with a risk penalty obtained from the GARCH model. This approach is generally easy in implementation and analysis when compared with other risk-averse models. Analysis is given to reveal the connection between our method and existing risk-averse RL methods. Experiment results on artificial data and real data in Hong Kong stock market are provided to compare the performances of our method and risk-sensitive RL algorithm and to illustrate the superiority of our method on generalization performance.