A DDPG Algorithm for Portfolio Management

Abstract

The purpose of portfolio management is to select a variety of financial products to form a portfolio and then manage these portfolios to achieve the purpose of diversifying risk and improving efficiency. In this paper, the Deep Deterministic Policy Gradient (DDPG) algorithm with neural networks is used, new states, actions and reward functions are proposed. The empirical analysis shows that this paper's method performs better than the method of investing with Q-learning algorithm, equally-weighted method, investing all funds in risk-free assets, or investing all funds in stocks.