The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Mispricing Factors
Open accessAbstract
A four-factor model with two "mispricing" factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four-and five-factor alternative models.Moreover, our size factor reveals a small-firm premium nearly twice usual estimates.The mispricing factors aggregate information across 11 prominent anomalies by averaging rankings within two clusters exhibiting the greatest return co-movement.Investor sentiment predicts the mispricing factors, especially their short legs, consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting.A three-factor model with a single mispricing factor also performs well, especially in Bayesian model comparisons.(JEL G12)
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
White, H. (1980) · Econometrica · doi:10.2307/1912934 · Cited by 25,861
Common risk factors in the returns on stocks and bonds
Fama, E. F., & French, K. R. (1993) · Journal of Financial Economics · doi:10.1016/0304-405x(93)90023-5 · Cited by 25,754
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 20,598
CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*
Sharpe, W. F. (1964) · The Journal of Finance · doi:10.1111/j.1540-6261.1964.tb02865.x · Cited by 19,441
On Persistence in Mutual Fund Performance
Carhart, M. M. (1997) · The Journal of Finance · doi:10.1111/j.1540-6261.1997.tb03808.x · Cited by 17,283
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
JEGADEESH, N., & TITMAN, S. (1993) · The Journal of Finance · doi:10.1111/j.1540-6261.1993.tb04702.x · Cited by 11,584
A five-factor asset pricing model
Fama, E. F., & French, K. R. (2015) · Journal of Financial Economics · doi:10.1016/j.jfineco.2014.10.010 · Cited by 7,852
Noise Trader Risk in Financial Markets
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990) · Journal of Political Economy · doi:10.1086/261703 · Cited by 6,234
Investor Sentiment and the Cross‐Section of Stock Returns
BAKER, M., & WURGLER, J. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.00885.x · Cited by 6,049
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Ohlson, J. A. (1980) · Journal of Accounting Research · doi:10.2307/2490395 · Cited by 5,989
Cited by
Characteristics are covariances: A unified model of risk and return
Kelly, B. T., Pruitt, S., & Su, Y. (2019) · Journal of Financial Economics · doi:10.1016/j.jfineco.2019.05.001 · Cited by 809
Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2023) · Management Science · doi:10.1287/mnsc.2022.4449 · Cited by 217
Factor Models, Machine Learning, and Asset Pricing
Giglio, S., Kelly, B., & Xiu, D. (2022) · Annual Review of Financial Economics · doi:10.1146/annurev-financial-101521-104735 · Cited by 187
Machine Learning Versus Economic Restrictions: Evidence from Stock Return Predictability
Avramov, D., Cheng, S., & Metzker, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3450322 · Cited by 74
Metadata
- Authors
- Stambaugh, R. F., & Yuan, Y.
- Venue
- The Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-12-31
- Volume
- 30
- Issue
- 4
- Pages
- 1270-1315
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W4243760387
- Cited by
- 894
- References
- 64
- FWCI
- 89.20
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mispricing Factors. https://app.cochat.ai/papers/10.1093/rfs/hhw107
- The paper
- Stambaugh, R. F., & Yuan, Y. (2017). Mispricing Factors. The Review of Financial Studies. https://doi.org/10.1093/rfs/hhw107
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
