1. Residual long short-term memory network with multi-source and multi-frequency information fusion: An application to China's stock market.
- Author
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Li, Songsong, Tian, Zhihong, and Li, Yao
- Subjects
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STOCK price forecasting , *STOCK exchanges , *ARTIFICIAL neural networks , *FEATURE selection , *STOCK price indexes , *DATA modeling - Abstract
• A 14-layer model with high predictive performance is proposed. • The model fuses three types of multi-source and multi-frequency features. • The model addresses three limitations of LSTM. • The model is validated through four comparison experiments on four stock indices. • Each component of the model improves the predictive performance. The most widely used model in stock price forecasting is the long short-term memory network (LSTM). However, LSTM has its limitations, as it does not recognize and extract features well and has a representational bottleneck. Furthermore, the factors affecting stock prices are multi-source and multi-frequency information, making neural network models difficult to handle. In this paper, we introduce a feature fusion residual LSTM (FFRL) model to answer these two questions – how to compensate for the three limitations of LSTM and how to fuse the multi-source and multi-frequency information. FFRL consists of three modules to improve the three limitations of LSTM, namely the feature selection module, feature extraction module, and residual module. To learn features from multi-source and multi-frequency information, FFRL applies the feature selection module to emphasize important features and the feature extraction module to extract deeper features. We demonstrate significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods on a variety of Chinese stocks. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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