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

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 high-frequency 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. This paper was accepted by William Lin Cong, finance. Funding: This work was supported by the National Key Research and Development Program of China [Grant 2022YFA1007900], the National Natural Science Foundation of China [Grants 12271013 and 72342004], and the Peking University’s Fundamental Research Funds for the Central Universities. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06215 .