A comparison between linear regression, lasso regression, decision tree, XGBoost, and RNN for asset price strategies

Shijie Li; Mingyu Si

doi:10.1117/12.2646634

23 August 2022 A comparison between linear regression, lasso regression, decision tree, XGBoost, and RNN for asset price strategies

Shijie Li, Mingyu Si

Author Affiliations +

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301A (2022) https://doi.org/10.1117/12.2646634
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

The stock market is an important part of the financial market, and its fluctuation is closely related to the market economy. Predicting stock prices is an important part of many analysts and researchers' work. Choosing the right investment strategy is a challenging task. Therefore, this paper uses the historical trading data of the four largest market capitalization companies on the NASDAQ exchange as research objects to predict and analyze their short-term price trends. According to the trading data, the methods of linear regression, lasso regression, decision tree, XGBoost and RNN are selected, and the accuracy and feasibility of investment strategies are compared horizontally. After empirical research, it is concluded that the RNN-based neural network model has good prediction accuracy. The results of this paper can provide a reference for stock market strategy selection to a certain extent.

Citation Download Citation

Shijie Li and Mingyu Si "A comparison between linear regression, lasso regression, decision tree, XGBoost, and RNN for asset price strategies", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301A (23 August 2022); https://doi.org/10.1117/12.2646634

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