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

Xunchao Qian ()
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Xunchao Qian: Nanjing University

A chapter in Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), 2026, pp 253-267 from Springer

Abstract: 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.

Keywords: Periodicity; Trading volume; Algorithmic trading (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-689-0_24

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DOI: 10.2991/978-94-6239-689-0_24

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