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

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

Author

Listed:
  • Xunchao Qian

    (Nanjing University)

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.

Suggested Citation

  • Xunchao Qian, 2026. "High-Frequency Periodicity in Trading Volume in the Chinese A-Share Market: Evidence from a Spectral Decomposition Approach," Advances in Economics, Business and Management Research, in: Ljiljana Trajkovic & José Alfredo F. Costa & Zaher Al Aghbari & Nor Azman Ismail & Dariusz Jacek Jak (ed.), Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), pages 253-267, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-689-0_24
    DOI: 10.2991/978-94-6239-689-0_24
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