Risk-sensitive policies for portfolio management

Abstract

The decision-making on portfolio investment is fundamental in the financial market, but getting the optimal strategy is challenging due to high uncertainty and massive noise in the market. Deep Deterministic Policy Gradient (DDPG), proposed by Lillicrap et al. (2015), is a deep Reinforcement Learning (RL) algorithm that made remarkable achievements in the financial perspective. Although the applications of RL in financial trading are well-developed, it is surprising that most of the literature ignores the possible risk of rare occurrences of catastrophic events and the effect of the worst-case scenarios on trading decisions. In this paper, we first develop a novel deep RL algorithm, called Hierarchical DDPG, that combines the classical DDPG algorithm and the Hierarchical RL structure to control the risk of portfolio investment. Second, we adapt the distributional DDPG method for portfolio management problems, which aims to maximize the α-percentile expectation based on the distribution of future returns. A real world dataset is used to validate the performance of our proposed models. The experimental results show that our proposed models outperform the market and classical DDPG, and moreover, both approaches provide effective methods of constructing a risk-sensitive policy to protect investors from suffering a huge loss.