A study of hybrid deep learning model for stock asset management

Abstract

Crafting a lucrative stock trading strategy is pivotal in the realm of investments. However, the task of devising such a strategy becomes challenging task the intricate and ever-changing situation of the stock market. In recent years, with the development of artificial intelligence (AI), some AI technologies have been proven to be successfully applied in stock price and asset management. For example, long short-term memory networks (LSTM) can be used for predicting stock price variation, reinforcement learning (RL) can be used for control stock trading, however, they are generally used separately and cannot achieve simultaneous prediction and trading. In this study, we propose a hybrid deep learning model to predict stock prices and control stock trading to manage assets. LSTM is responsible for predicting stock prices, while RL is responsible for stock trading based on the predicted price trends. Meanwhile, to reduce uncertainty in the stock market and maximize stock assets, the proposed LSTM model can predict the average directional index (ADX) to comprehend the stock trends in advance and we also propose several constraints to assist assets management, thereby reducing the risk and maximizing the stock assets. In our results, the hybrid model yields an average R2 value of 0.94 when predicting price variations. Moreover, employing the proposed approach, which integrates ADX and constraints, the hybrid model augments stock assets to 1.05 times than initial assets.

Keywords

• Long short-term memory
• Reinforcement learning
• Proximal policy optimization
• Stock prediction
• Stock trading