We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor’s quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should require a greater factor premium when they are more exposed to that factor after noise traders sell lots of stocks with high exposures to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.
Notre Dame, Johns Hopkins Carey, RUC-VUW Joint Virtual Research Workshop, 6th Annual Wolfe Global Quantitative and Macro Investment Conference, Federal Reserve Board, Campbell & Company, MFA Annual Meeting 2023, Southern Methodist University, FMCG 2023, SoFiE 2023 Conference, CICF 2023, Chinese University of Hong Kong, City University of Hong Kong, 10th SAFE Asset Pricing Workshop, UT Dallas 2023 Fall Finance Conference,36th Mitsui Life Symposium: New Frontiers in Asset Pricing, NFA Annual Meeting 2024, NBER Asset Pricing Program Meeting (Fall 2024)
Financial Markets and Corporate Governance Conference Runner-up for Best Paper