An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model

Abstract

The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield.