Information Diffusion and Intra-daily Volume Forecasting in Large Panels

Intra-daily trading volume forecasts are a key input for algorithmic trade execution. We introduce a forecasting methodology for large panels of assets that combines factor models with sparse vector autoregressions. The approach captures both market-wide common factors driving trading activity and the sparse network of volume spillovers among individual assets, consistent with a framework where information diffuses sequentially from leader to lagger stocks. We apply the methodology to a panel of 600 constituents of the STOXX 600 index spanning 16 European countries. We assess both the statistical accuracy and the economic value of the forecasts through an out-of-sample prediction exercise and a VWAP trade execution analysis. Results show that our methodology delivers signi cant statistical and economic gains relative to standard univariate benchmarks. The improvements are heterogeneous: the largest gains accrue to stocks with high network connectivity, while rm size also plays a signi cant role, consistent with the information diffusion mechanism underlying our approach.