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 (β) 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 demand a higher factor premium when they have absorbed noise trading flows of stocks with high loadings 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.
Selected conference presentations
American Finance Association (AFA) Annual Meeting, Philadelphia — January 2026
SFS Cavalcade North America, Stevens Institute of Technology — May 2025
NBER Asset Pricing Program Meeting, Stanford University — November 2024
Under the earlier title “A Factor Framework for Cross-Sectional Price Impacts”:
University of Michigan Mitsui Finance Symposium, “New Frontiers in Asset Pricing” — May 2024
FMA Applied Finance Conference, New York — May 2024
Society for Financial Econometrics (SoFiE) Annual Meeting, Seoul — June 2023
Source: Yinan Su’s CV, conference and seminar presentations.
Yu An (Johns Hopkins Carey Business School); Yinan Su (Johns Hopkins University, Carey Business School); Chen Wang (University of Notre Dame, Mendoza College of Business)
Status
Revision requested by the Journal of Financial Economics
quantity; flow; noise trader; risk and return; cross section of return; return prediction; factor zoo; Lasso; PCA; BTQ
Cite this paper
An, Yu, Yinan Su, and Chen Wang. “Quantity, Risk, and Return.” Working paper, February 17, 2025. SSRN 4098609.
BibTeX
@techreport{an_su_wang_2025_qrr,
title = {Quantity, Risk, and Return},
author = {An, Yu and Su, Yinan and Wang, Chen},
year = {2025},
month = feb,
institution = {SSRN},
number = {4098609},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4098609}
}
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Suggested AI reading prompt
Read and explain the research paper "Quantity, Risk, and Return". Use the author’s paper page for context, publication details, and any linked coverage: https://www.suyinan.com/papers/quantity-risk-and-return/. Read the full paper in arXiv HTML here: https://arxiv.org/html/2609.05162. Begin your explanation with the paper’s motivation: what question it asks, why that question is important, and what gap in existing understanding makes it worth studying. Then explain its original contribution, strongest insights, methodology, principal findings, and broader implications for research or practice. Give a clear, engaging account grounded in the paper, attribute external commentary to its source, and distinguish the authors’ claims from your interpretation. Support assessments of novelty and significance with evidence, and cite relevant sections, equations, tables, or figures where helpful.