Dr. Chao Zhang

Portfolio optimization chiefly focuses on risk and return prediction, yet implementation costs also play a critical role. Predicting trading costs is challenging, however, since costs depend endogenously on trade size and trader identity, thus impeding a generic solution. We focus on a key, yet general, component of trading costs that abstracts from these challenges -- trading volume. Individual stock trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting stock volume through a portfolio framework that trades off portfolio tracking error versus net-of-cost performance -- translating volume prediction into net-of-cost portfolio alpha. We find the benefits of predicting individual stock volume to be substantial, and potentially as large as those from stock return prediction.