A stock volatility prediction using hybrid machine learning models

Ruosen Yang; Sam Du; JianPeng Huang; Yuteng Zhang

doi:10.1117/12.2647212

23 August 2022 A stock volatility prediction using hybrid machine learning models

Ruosen Yang, Sam Du, JianPeng Huang, Yuteng Zhang

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301T (2022) https://doi.org/10.1117/12.2647212
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

With the development of social economy, people's concept of investment and financial management has gradually strengthened. Machine learning can be applied in the volatility. In our paper, we use hybrid model based on XGBoost, LightGBM and TabNet for predicting realized volatility. We conclude some related works and represent our hybrid model in the detail. In the experiment process, we compare our hybrid model with other models, and the result shows that our hybrid model owns the lowest RMSPE score 0.198 among all compared models.

Citation Download Citation

Ruosen Yang, Sam Du, JianPeng Huang, and Yuteng Zhang "A stock volatility prediction using hybrid machine learning models", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301T (23 August 2022); https://doi.org/10.1117/12.2647212

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