Download This PaperTrading Volume Alpha
50 Pages Posted: 23 Apr 2024
Date Written: April 21, 2024
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. Trading volume is highly predictable, especially with machine learning. We model the economic benefits of predicting volume through a portfolio framework that trades off portfolio tracking error versus net-of-cost performance---in essence translating volume prediction into net-of-cost portfolio alpha. We find the benefits of predicting volume to be substantial, and potentially as large as those from return prediction.
Keywords: machine learning, AI, neural networks, portfolio optimization, trading volume, trading costs, investments
JEL Classification: C45, C53, C55, G00, G11, G12
Suggested Citation: Suggested Citation
Yinan Su (Contact Author)
Johns Hopkins University - Carey Business School ( email )
100 International Drive
Baltimore, MD 21202
United States