Residual long short-term memory network with multi-source and multi-frequency information fusion: An application to China?s stock market

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 multifrequency 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a feature fusion residual LSTM (FFRL) model to address the limitations of LSTM and handle multi-source and multi-frequency information. FFRL consists of three modules, namely the feature selection module, feature extraction module, and residual module, to improve the limitations of LSTM. Significant performance improvements of FFRL over comparison models, ablation networks, and visualization methods are demonstrated on a variety of Chinese stocks.