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Forecasting Intraday Volume in Equity Markets with Machine Learning

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2505.08180

Authors: Cucuringu, Mihai, Li, Kang, Zhang, Chao

Institutions: University of Oxford, The Hong Kong University of Science and Technology (Guangzhou)

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6910891111

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