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Academic Journal of Computing & Information Science, 2023, 6(10); doi: 10.25236/AJCIS.2023.061004.

Research on analysis and application of quantitative investment strategies based on deep learning


Zhou Senxin, Zhang Sitong, Xie Linna

Corresponding Author:
Zhou Senxin

Anhui University of Finance and Economics, Bengbu, Anhui Province,233000, China


Due to the dynamics and complexity of the stock market, stock prediction models may encounter some challenges in predicting future stock movements, resulting in their poor generalisation ability. This paper discusses the application and effectiveness of deep learning technology in the financial field by studying the quantitative investment strategy based on deep learning. First, theoretical foundations of deep learning are introduced. Then, the methods for constructing quantitative investment strategies based on Long Short-Term Memory Network (LSTM) are elaborated, including data preprocessing, model selection and training, and strategy execution. Next, the performance and stability of the strategy are evaluated through backtesting and empirical analysis of historical data. Finally, the research results are summarized, and the direction of further research and application is prospected.


Quantitative investment, Deep learning, Strategy analysis, Performance evaluation

Cite This Paper

Zhou Senxin, Zhang Sitong, Xie Linna. Research on analysis and application of quantitative investment strategies based on deep learning. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 10: 24-30. https://doi.org/10.25236/AJCIS.2023.061004.


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