This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value of 0.476225 (p < 0.001), which splits the sample into distinct uncertainty regimes. Logistic regression incorporating this threshold reveals that the joint condition of high opening volume and low IVU (high uncertainty) significantly amplifies the predictive power of initial returns, achieving 63.04% accuracy in the high-uncertainty, high-volume regime. XGBoost further captures complex non-linear interactions, with IVU-related features ranking among the most important predictors and achieving 71.43% out-of-sample accuracy under high-volume, high-uncertainty conditions. A machine learning trading strategy leveraging these predictions yields a total return of 117.99% with a Sharpe ratio of 3.02 over seven years, significantly outperforming benchmarks. Our findings highlight information uncertainty as a critical moderator of intraday momentum and a valuable source of actionable alpha.
Yang et al. (Sat,) studied this question.