Forecasting Intraday Volume in Equity Markets with Machine Learning
By: Mihai Cucuringu, Kang Li, Chao Zhang
Potential Business Impact:
Predicts stock trading amounts to help investors make money.
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.
Similar Papers
Cryptocurrency Price Forecasting Using Machine Learning: Building Intelligent Financial Prediction Models
Machine Learning (CS)
Predicts crypto prices better using liquidity measures
Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation
Statistical Finance
Finds what makes stock markets crash.
Data-Efficient Realized Volatility Forecasting with Vision Transformers
Machine Learning (CS)
Helps predict stock price changes using past data.