Abstract:

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.

Short Bio:

Dr. Chao Zhang joined the Fintech thrust in HKUST(GZ) as an assistant professor in July 2024. Prior to this position, he was a postdoctoral researcher at the Oxford-Man Institute of Quantitative Finance, University of Oxford. Dr. Zhang obtained his DPhil from University of Oxford, mentored by Prof. Rama Cont and Prof. Mihai Cucuringu, and holds an M.Res., and a B.S. from Peking University. His research interests primarily lie in Machine Learning + Finance, focusing on large language models, asset pricing, volatility modeling, generative AI, graph structure, price impact, etc. His work has been published in esteemed conferences and journals, such as NeurIPS, Quantitative Finance, Journal of Financial Econometrics, International Journal of Forecasting, Review of Asset Pricing Studies, etc. Dr. Zhang’s DPhil thesis received the prestigious G-Research 1st PhD prize in Maths and Data Science at Oxford. His research has garnered significant attention from the industry, leading to invited talks at institutions such as Goldman Sachs, Bank of America, MAN Group, and Intesa Sanpaolo.

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