Prediction of stock return by LSTM neural network

ABSTRACT

The role of the stock market in the whole financial market is indispensable. How to obtain the actual trading income and maximize the interests in the trading process has been a problem studied by scholars and financial practitioners for a long time. Deep learning network can extract features from a large number of original data, which has potential advantages for stock market prediction. Based on the Shanghai and Shenzhen stock markets from 2019 to 2021, we use LSTM models, optimized on in-sample period and tested on out-of-sample period, using rolling window approach. We select the right hyperparameters at the beginning of our tests, use RBM preprocessing data, then use LSTM model to obtain expected stock return, to effectively predict future stock market analysis and predictive behavior. Finally, we perform a sensitivity analysis of the main parameters and hyperparameters of the model.

Acknowledgments

The work is supported by the following project grants, National Social Science Foundation funded project Research on vulnerability diagnosis and ‘data governance’ model of urban drainage system（17BGL210）; Tianjin’s key soft science research project Research on Countermeasures to vigorously promote global science popularization in Tianjin（19ZLZDZF00270）.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

All employed data will be made available on reasonable request. http://data.cnstock.com/

Additional information

Funding

This work was supported by National Social Science Foundation funded project Research on vulnerability diagnosis and ‘data governance’ model of urban drainage system（17BGL210）; Tianjin’s key soft science research project Research on Countermeasures to vigorously promote global science popularization in Tianjin（19ZLZDZF00270）.