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.

Can we explain asset price movements even with ex post observations of their drivers? Reinterpreting Roll's (1988) AFA Presidential Address, "R²," as a modern empirical exercise, we show that conventional macroeconomic, cash-flow, expectations, and flow variables deliver out-of-sample R² values that are often negative and otherwise below 10% for monthly U.S. equity returns and are unavailable at the daily frequency. We use generative AI to extract newspaper-stated reasons from Wall Street Journal market summaries and filter out price and return language. These reasons explain around 30–40% of daily and monthly returns out of sample. Their content maps to interpretable themes, including corporate news, macroeconomic conditions, monetary policy, sentiment, and geopolitics.