Portfolio construction based on stock returns prediction using machine learning

Sinan Liu; Zihan Wu; Xianyi Zhang

doi:10.1117/12.2646573

23 August 2022 Portfolio construction based on stock returns prediction using machine learning

Sinan Liu, Zihan Wu, Xianyi Zhang

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233024 (2022) https://doi.org/10.1117/12.2646573
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

The stock market’s prediction and portfolio construction afterward have been seen as important and effective since Markowitz’s portfolio theory. While many models of prediction and portfolio combination exist, the most appropriate prediction models for specific firms are not definite. In this paper, the stock price of six leading enterprises in different fields were predicted by four statistical models including Multiple linear regression (MLR), K-Nearest Neighbor (kNN), Auto-Regressive Integrated Moving Averages (Auto-ARIMA) and Long Short Term Memory Networks (LSTMs). Crossvalidation was conducted using model assessment statistics R2, RMSE and sMAPE to compare the prediction performance of each model. The result revealed that LSTM performs the best among the four models on the six selected firms. The expected return generated by LSTM was then used to construct the portfolio model by allocating risks and returns. The final portfolio has shown the detailed distribution of each firm’s stock. Researchers can do further refinements by adding more models and firms based on the methodology in this paper.

Citation Download Citation

Sinan Liu, Zihan Wu, and Xianyi Zhang "Portfolio construction based on stock returns prediction using machine learning", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233024 (23 August 2022); https://doi.org/10.1117/12.2646573

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