Trading Volume Alpha

Ruslan Goyenko, Bryan T. Kelly, Tobias J. Moskowitz, Yinan Su and Chao Zhang

No 33037, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C45 C53 C55 G00 G11 G12 G17 (search for similar items in EconPapers)
Date: 2024-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
Note: AP
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