SMPDF: stock movement prediction based on stock prices and text

Abstract

Stock movement prediction is a challenging task due to its dynamic and nonlinear characteristics. Different information sources provide a rich variety of perspectives and dimensions, which can be integrated to understand market dynamics more comprehensively. However, different information may have complex interrelationships and nonlinear features, the deep fusion method can make the model have a powerful representation learning ability, and automatically learn complex features and relationships. Therefore, how to deeply fuse numerical data and text to achieve interaction between different types of information to complete more effective prediction tasks is a difficult problem. In this paper, we propose a stock movement prediction model based on deep fusion (SMPDF), including text and stock price feature extraction, feature fusion and feature processing, which can successfully fuse text and stock price. Experiments show that the proposed method has better modeling performance under the SMPDF framework and brings greater improvement in prediction performance.

Keywords:

• Stock prediction
• deep fusion
• text processing

Acknowledgments

The authors are thankful for the anonymous referee's constructive comments.

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.

Notes

1 https://github.com/yumoxu/stocknet-dataset.

2 https://github.com/wuhuizhe/CHRNN.

3 https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DPQMQH.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972227 and 72171133), Natural Science Foundation of Shandong Province (ZR2022MF245 and ZR2020MA036), Youth Innovation Team in Colleges and universities of Shandong Province (2022KJ185), Youth Talent Introduction and Cultivation Plan in Colleges and Universities of Shandong Province (Image Processing and Data Mining Team) and in part by the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.