Modeling Low-risk Actions from Multivariate Time Series Data using Distributional Reinforcement Learning

Abstract

In recent years, investment strategies in financial markets using deep learning have attracted a significant amount of research attention. The objective of these studies is to obtain investment behavior that is low risk and increases profit. Although Distributional Reinforcement Learning (DRL) expands the action-value function to a discrete distribution in reinforcement learn- ing which can control risk, DRL has not yet been used to learn investment action. In this study, we construct a low-risk investment trading model using DRL. This model is back-tested on Nikkei 225 data and compared with Deep Q Network (DQN). We evaluate the performance in terms of final asset amount, standard deviation, and the Sharpe ratio. The experimental results show that the proposed method can learn low-risk actions with the increasing profit, outperforming the compared method DQN.