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28 March 2023 Research on LSTM multi-factor quantitative stock selection strategy based on attention mechanism
Zezhong Li
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Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256639 (2023) https://doi.org/10.1117/12.2668783
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
In this paper, monthly frequency multi-factor data on valuation, momentum, turnover rate and technology of A-share listed companies in Shanghai and Shenzhen markets from January 2012 to July 2022 are selected and input to LSTM and LSTM model with fused attention mechanism respectively for training after data pre-processing. The sector-neutral layered-portfolios and the sector-neutral stock selection portfolios were constructed based on the model output, respectively. In the model evaluation section, it is confirmed that the Attention-LSTM model outperforms the LSTM model in predicting stock ups and downs. The single-factor layered back test under monthly position adjustment and stock selection strategy back test confirmed that the Attention-LSTM model significantly outperformed the LSTM model in terms of annualized return, sharpe ratio, and maximum retracement, and also significantly outperformed the CSI 300 and CSI 500.
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Zezhong Li "Research on LSTM multi-factor quantitative stock selection strategy based on attention mechanism", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256639 (28 March 2023); https://doi.org/10.1117/12.2668783
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KEYWORDS
Data modeling
Neural networks
Machine learning
Deep learning
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