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Proceedings of the 3rd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2024, September 6–8, 2024, Jinan, China

Research Article

Quantitative Research on Stock Multi-Factor Models in Empirical Investment

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  • @INPROCEEDINGS{10.4108/eai.6-9-2024.2353667,
        author={Chengzhao  Zhang and Xun  Huang and Hairong  Li and Jingxin  Zhao},
        title={Quantitative Research on Stock Multi-Factor Models in Empirical Investment},
        proceedings={Proceedings of the 3rd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2024, September 6--8, 2024, Jinan, China},
        publisher={EAI},
        proceedings_a={ICPDI},
        year={2024},
        month={12},
        keywords={multi-factor; quantitative stock investment; volatility; portfolio; csi300},
        doi={10.4108/eai.6-9-2024.2353667}
    }
    
  • Chengzhao Zhang
    Xun Huang
    Hairong Li
    Jingxin Zhao
    Year: 2024
    Quantitative Research on Stock Multi-Factor Models in Empirical Investment
    ICPDI
    EAI
    DOI: 10.4108/eai.6-9-2024.2353667
Chengzhao Zhang1, Xun Huang2,*, Hairong Li2, Jingxin Zhao2
  • 1: Chengdu Polytechnic
  • 2: Chengdu University
*Contact email: huangxun1118@126.com

Abstract

This study aims to empirically examine the efficacy of a multi-factor model in quantitative stock investment. The model integrates various factors such as value, size, momentum, and volatility, which are well-established in academic research and have been shown to significantly influence stock returns. Utilizing a comprehensive dataset of stock market information, we establish factor portfolios based on these factors and evaluate their performance over a specific time frame. The empirical findings indicate that the multi-factor model surpasses conventional single-factor models in terms of risk-adjusted returns. Specifically, the value and momentum factors demonstrate notable positive alphas, signifying their capacity to generate excess returns beyond market risk considerations. Additionally, the size and volatility factors also exhibit substantial impacts on stock returns, further underscoring the efficacy of the multi-factor model in capturing stock price movements. During a specific time period, this study focused on the constituents of the CSI300 Index (China Securities Index 300) and selected 12 factors, including industry, technical, and financial factors, to establish an initial factor pool. Subsequently, a multi-factor stock selection model based on neural networks was developed and backtested. The empirical results demonstrated that this investment strategy yielded higher excess returns compared to the benchmark index.

Keywords
multi-factor; quantitative stock investment; volatility; portfolio; csi300
Published
2024-12-16
Publisher
EAI
http://dx.doi.org/10.4108/eai.6-9-2024.2353667
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