Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)

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High-Frequency Periodicity in Trading Volume in the Chinese A-Share Market: Evidence from a Spectral Decomposition Approach

Authors
Xunchao Qian1, *
1Nanjing University, Nanjing, 210093, China
*Corresponding author. Email: 502023150033@smail.nju.edu.cn
Corresponding Author
Xunchao Qian
Available Online 28 May 2026.
DOI
10.2991/978-94-6239-689-0_24How to use a DOI?
Keywords
Periodicity; Trading volume; Algorithmic trading
Abstract

The proliferation of algorithmic trading has reshaped market microstructure, manifesting as periodic fluctuations in high-frequency trading volume series. Based on tick-by-tick data for all A-share stocks in China from 2017 to 2025, this paper constructs a spectral decomposition model tailored to 100ms high-frequency series to systematically identify and measure the periodicity intensity of trading volume. The study reveals five strongest periodic frequencies—3s, 1.5s, 1s, 0.5s, and 0.25s—in the A-share market in recent years, and this high-frequency periodicity exists significantly in the majority of stocks. Further analysis demonstrates a strong positive correlation between periodicity intensity and algorithmic trading activity, and stocks with stronger periodicity exhibit higher price efficiency.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)
Series
Advances in Economics, Business and Management Research
Publication Date
28 May 2026
ISBN
978-94-6239-689-0
ISSN
2352-5428
DOI
10.2991/978-94-6239-689-0_24How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xunchao Qian
PY  - 2026
DA  - 2026/05/28
TI  - High-Frequency Periodicity in Trading Volume in the Chinese A-Share Market: Evidence from a Spectral Decomposition Approach
BT  - Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)
PB  - Atlantis Press
SP  - 253
EP  - 267
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-689-0_24
DO  - 10.2991/978-94-6239-689-0_24
ID  - Qian2026
ER  -