A hybrid stock market prediction model based on GNG and reinforcement learning

The stock market is a dynamic, complex, and chaotic environment, which makes predictions for the stock market difficult. Many prediction methods are applied to the stock market, but most are supervised learning and cannot effectively parse the trading information present in the stock market. This paper proposes a prediction model that combines unsupervised learning with reinforcement learning to address this problem. Firstly, we capture the stock trend from historical stock data and construct the trading environment state of the market by the growing neural gas (GNG) algorithm in unsupervised learning. Secondly, the reward function is restructured to provide timely feedback on the trading information present in the stock trading market. Finally, a novel trading agent algorithm, Triple Q-learning, is designed to execute the corresponding trading behavior and make comprehen-sive predictions of the stock market based on the environment state constructed by GNG. Experimental results on several stock datasets demonstrate that the proposed model outperforms other comparative models in this paper.

Automated summary

This paper proposes a prediction model that combines unsupervised learning with reinforcement learning to address the difficulty of predicting the stock market accurately. The model captures the stock trend from historical data and constructs the trading environment state using unsupervised learning algorithm, then uses a novel trading agent algorithm, Triple Q-learning, to execute trading behaviors and make comprehensive predictions. Experimental results show that the proposed model outperforms other comparative models.