Spectral Volume Models: Universal High-Frequency Periodicities in Intraday Trading Activities

• 成果类型: Article
• 署名作者: Wu, Lintong; Zhang, Ruixun; Dai, Yuehao
• 署名单位: Peking University; Peking University; Peking University; Peking University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.06215
• 发表日期: 2026
• 关键词: TRADING VOLUME; periodicity; Algorithmic trading; VWAP execution; Price informativeness; excess return

摘要

We develop spectral volume models to systematically estimate, explain, and exploit the high-frequency periodicity in intraday trading activities using Fourier analysis. The framework consistently recovers periodicities at specific frequencies in three steps, despite their low signal-to-noise ratios. This reveals persistent and universal highfrequency periodicities in the United States and Chinese stock markets in recent years, and the dominant frequencies explain a significant fraction of the total variance of intraday volumes. We provide evidence that this phenomenon likely reflects the behaviors of trading algorithms with repeated and regular trading instructions. Finally, we demonstrate that uncovering such high-frequency periodicities improves intraday volume predictions and volume weighted average price execution qualities, yields insights for price informativeness of algorithmic trading, and generates excess returns.