Application of Nonparametric Statistics on Stock Trading Volume Distribution

Yaqiang Fan; Ximing Cheng

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

Download46 downloads

Cite: BibTeX Plain Text

@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.

Keywords: stock trading volume; nonparametric statistics; kernel density estimation; kolmogorov-smirnov test

Published: 2024-06-07
Publisher: EAI

: http://dx.doi.org/10.4108/eai.29-3-2024.2347465