Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29–31, 2024, Wuhan, China

Research Article

Application of Nonparametric Statistics on Stock Trading Volume Distribution

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347465,
        author={Yaqiang  Fan and Ximing  Cheng},
        title={Application of Nonparametric Statistics on Stock Trading Volume Distribution},
        proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2024},
        month={6},
        keywords={stock trading volume; nonparametric statistics; kernel density estimation; kolmogorov-smirnov test},
        doi={10.4108/eai.29-3-2024.2347465}
    }
    
  • Yaqiang Fan
    Ximing Cheng
    Year: 2024
    Application of Nonparametric Statistics on Stock Trading Volume Distribution
    ICBBEM
    EAI
    DOI: 10.4108/eai.29-3-2024.2347465
Yaqiang Fan1,*, Ximing Cheng1
  • 1: Beijing Information Science and Technology University
*Contact email: 2022021058@bistu.edu.cn

Abstract

Stock trading volume, as a key indicator reflecting market trading activity, is widely used in market trend analysis and investment strategy formulation. In recent years, many scholars have conducted research on it, and the existing methods mainly include parameter statistics and machine learning algorithms. This article applies nonparametric statistical methods such as kernel density estimation and the single sample Kolmogorov- Smirnov (KS) test to analyze the daily hourly trading volume of A-shares. It is found that the Johnson-U distribution has the best fit to the data, which is of great significance for understanding and analyzing the trading behavior of the stock market.