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Forecasting Intraday Volume in Equity Markets with Machine Learning

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  • Mihai Cucuringu
  • Kang Li
  • Chao Zhang

Abstract

This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading volume is highly predictable, especially with ML and considering commonality. Additionally, we assess the economic benefits of accurate volume forecasting through Volume Weighted Average Price (VWAP) strategies. The results demonstrate that precise intraday forecasting offers substantial advantages, providing valuable insights for traders to optimize their strategies.

Suggested Citation

  • Mihai Cucuringu & Kang Li & Chao Zhang, 2025. "Forecasting Intraday Volume in Equity Markets with Machine Learning," Papers 2505.08180, arXiv.org.
    • Handle: RePEc:arx:papers:2505.08180
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    References listed on IDEAS

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