Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning
This addresses portfolio optimization for investors in financial markets, but it is incremental as it applies existing deep reinforcement learning methods to stock data.
The paper tackled portfolio management in stock markets by building a system using deep reinforcement learning to learn factor representations and make optimal portfolio choices among S&P500 stocks, resulting in performance that significantly outperforms the average market.
Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward. Deep reinforcement learning provides good opportunity to model the complexity of portfolio choice in high-dimensional and data-driven environment by leveraging the powerful representation of deep neural networks. In this paper, we build a portfolio management system using direct deep reinforcement learning to make optimal portfolio choice periodically among S\&P500 underlying stocks by learning a good factor representation (as input). The result shows that an effective learning of market conditions and optimal portfolio allocations can significantly outperform the average market.