Research on investment strategies of stock market based on sentiment indicators and deep reinforcement learning

Peng Zhou; Jinglin Tang; Yizhuo Li

doi:10.1117/12.2627792

22 April 2022 Research on investment strategies of stock market based on sentiment indicators and deep reinforcement learning

Peng Zhou, Jinglin Tang, Yizhuo Li

Author Affiliations +

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634E (2022) https://doi.org/10.1117/12.2627792
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

With the application of artificial intelligence in the financial field, quantitative trading is considered to be profitable. Based on this, this paper proposes an improved deep recurrent DRQN-ARBR model because the existing quantitative trading model ignores the impact of irrational investor behavior on the market, making the application effect poor in an environment where the stock market in China is non-efficiency. By changing the fully connected layer in the original model to the LSTM layer and using the emotion indicator ARBR to construct a trading strategy, this model solves the problems of the traditional DQN model with limited memory for empirical data storage and the impact of observable Markov properties on performance. At the same time, this paper also improved the shortcomings of the original model with fewer stock states and chose more technical indicators as the input values of the model. The experimental results show that the DRQN-ARBR algorithm proposed in this paper can significantly improve the performance of reinforcement learning in stock trading.

Citation Download Citation

Peng Zhou, Jinglin Tang, and Yizhuo Li "Research on investment strategies of stock market based on sentiment indicators and deep reinforcement learning", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634E (22 April 2022); https://doi.org/10.1117/12.2627792

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KEYWORDS

Data modeling

Performance modeling

Autoregressive models

Data storage

Visual process modeling

Artificial intelligence

Neural networks

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