Comparison Between Logistic Regression, LSTM, and Random Forest in Chinese Stock Prediction

Shiming Zhou

doi:10.54097/kr7rbj10

Authors

Shiming Zhou

DOI:

https://doi.org/10.54097/kr7rbj10

Keywords:

Stock Prediction, linear models, decision trees, neural network models.

Abstract

T Machine learning is transforming industries with its ability to derive insights and patterns within massive datasets. Among numerous algorithms available, certain foundational models stand out due to their efficiency and capability. This article compares three such models: linear models, decision trees, and neural network models. While all three has its unique pros and cons, this article aims to guide readers in choosing most fitting model with their tasks, by clarify the difference and future outlooks of these models.

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References

F. Braudel and S. Reynolds, The wheels of Commerce (University of California Press, Los Angele, 1992) pp. 1 - 221.

E. Chancellor, Devil take the hindmost: A history of financial speculation, (Plume, New York, 2005) pp. 10 - 24.

A. Cowles, Econometrica 1(3), 309 (1933)

E. F. Fama, The Journal of Finance 25 (2), 383 (1970).

E. Schöneburg, Neurocomputing 2 (1), 17 – 27 (1990).

K. Chen, Y. Zhou and F. Dai, “A LSTM-based method for stock returns prediction: A case study of china stock market,” 2015 IEEE International Conference on Big Data (Big Data) p. 145.

L. Chen, Z. Qiao, M. Wang, et al., IEEE Access 6, 48625 – 48633 (2018).

S. Basak, S. Kar, S. Saha, L. Khaidem and S. R. Dey, The North American Journal of Economics and Finance 47, 552 – 567 (2019).

Q. Ding, S. Wu, H. Sun, and J. Guo, “Hierarchical multi-scale gaussian transformer for stock movement prediction,” Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (2020) p. 17.

W. Zhang, T. Yin, Y. Zhao, B. Han and H. Liu, IEEE Access 11, 14115 – 14127 (2023).