Portfolio optimization focuses on risk and return prediction, yet implementation costs critically matter. Predicting trading costs is challenging because costs depend on trade size and trader identity, thus impeding a generic solution. We focus on a component of trading costs that applies universally ? trading volume. Individual stock trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting volume through a portfolio framework that trades off tracking error versus net-of-cost performance ? translating volume prediction into net-of-cost alpha. The economic benefits of predicting individual stock volume are as large as those from stock return predictability.