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Academic Journal of Computing & Information Science, 2021, 4(6); doi: 10.25236/AJCIS.2021.040603.

Quantified Investment Strategies and Excess Returns: Stock Price Forecasting Based on Machine Learning


Zihe Tang1, Yanqi Cheng2, Ziyao Wang3

Corresponding Author:
Zihe Tang

1Department of Mathematics, University of California, Los Angeles, California, United States    

2Department of Economics, University of California, San Diego, California, United States   

3Department of Corporate Finance, University of International Business and Economics, Beijing, China 

These authors contributed equally to this work


Under frequent trading strategy, this paper assumes that rational investors will adopt a long-term conservative frequent trading strategy. By studying historical data by machine learning, we predict the future trend of stock prices and compare the returns of frequent trading strategy and long-term holding outcomes. The results show that the trend of stock prices is the key factor affecting quantitative investment strategy. This study enriches the relevant literature in the field of quantitative investment and focuses on the comparison of returns between frequent trading strategies and long-term holding strategies. It provides standards for selecting investment strategies in the securities market. In the practical sense, this paper proposes investment suggestions for investors to benefit from market fluctuations.


Quantitative Investment, Linear Regression Machine Learning Model, Long-Term Conservative Frequent Trading Strategy

Cite This Paper

Zihe Tang, Yanqi Cheng, Ziyao Wang. Quantified Investment Strategies and Excess Returns: Stock Price Forecasting Based on Machine Learning. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 6: 10-14. https://doi.org/10.25236/AJCIS.2021.040603.


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