Yield Forecasting by Machine Learning Algorithm: Evidence from China’s A-share Market

Yue Hu, Haosheng Guo, Wenli Huang and Yueling Xu

Emerging Markets Finance and Trade, 2023, vol. 59, issue 6, 1767-1781

Abstract: This study uses five machine learning algorithms (Stochastic gradient descent (SGD), Decision tree, Random forest, Gradient boosting decision tree (GBDT), and Convolutional neural networks (CNN)) to explore their prediction effects on China’s stock market. It constructs a monthly rolling model for stock return prediction. Selecting stocks of the CSI 300 index from January to June 2021 as specific monthly samples and classifying three factors – fundamentals, volatility(risk) and technical indicators, the results demonstrate that (1) machine learning brings favorable investment returns in simulated quantitative trading of China’s A-share market (2) the technical indicator factor is the most valuable, with the momentum technical factor having greatest influence, followed by the volatility (risk) factor and fundamental factors. Therefore, this study has a critical reference value and is significant in guiding yield forecasting in intricate stock markets.

Date: 2023
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DOI: 10.1080/1540496X.2022.2148464

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