Hostname: page-component-5db58dd55d-mhzq2 Total loading time: 0 Render date: 2026-06-05T01:24:07.276Z Has data issue: false hasContentIssue false
  • English
  • Français

Zeroing In on the Expected Returns of Anomalies

Part of: JFQA William F. Sharpe Best Paper Award Winner

Published online by Cambridge University Press:  12 August 2022

Andrew Y. Chen  and
Mihail Velikov Open the ORCID record for Mihail Velikov [Opens in a new window]
Andrew Y. Chen
Affiliation:
Federal Reserve Board Research and Statistics andrew.y.chen@frb.gov
Mihail Velikov*
Affiliation:
Pennsylvania State University Smeal College of Business
*
velikov@psu.edu (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid–ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly’s expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 3 , May 2023 , pp. 968 - 1004
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

This article originated from a conversation with Svetlana Bryzgalova. We thank Marie Briere (HFPE discussant), Jennifer Conrad (the editor), Victor DeMiguel, Yesol Huh, Markus Ibert, Nina Karnaukh, Alberto Martin-Utrera (FDU discussant), R. David McLean (the referee), Andy Neuhierl, Steve Sharpe, Nitish Sinha, Ingrid Tierens (Jacobs Levy discussant), Tugkan Tuzun, Michael Weber, Haoxiang Zhu, and seminar participants at the Federal Reserve Board, Penn State University, University of Georgia, the 11th Annual Hedge Fund and Private Equity Research Conference, 2019 Finance Down Under Meetings, 2019 Eastern Finance Association Meetings, 2019 Jacobs Levy Frontiers in Quantitative Finance conference, and 2020 INFORMS Annual Meeting for helpful comments. We are grateful to Victor DeMiguel, Alberto Martin-Utrera, Francisco Nogales, and Raman Uppal for making their data available to us and we thank Rebecca John for excellent research assistance. The views expressed herein are those of the authors and do not necessarily reflect the position of the Board of Governors of the Federal Reserve or the Federal Reserve System.

References

Abdi, F., and Ranaldo, A.. “A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices.” Review of Financial Studies, 30 (2017), 4437–4480.CrossRefGoogle Scholar
An, L.; Huang, S.; Lou, D.; and Shi, J.. “Why Don’t Most Mutual Funds Short Sell?” Available at SSRN 3813790 (2021).CrossRefGoogle Scholar
Asquith, P.; Pathak, P. A.; and Ritter, J. R.. “Short Interest, Institutional Ownership, and Stock Returns.” Journal of Financial Economics, 78 (2005), 243–276.CrossRefGoogle Scholar
Azevedo, E. M.; Deng, A.; Olea, J. L. M.; and Weyl, E. G.. “Empirical Bayes Estimation of Treatment Effects with Many A/B Tests: An Overview.” AEA Papers and Proceedings, 109 (2019), 43–47.CrossRefGoogle Scholar
Ball, R.; Kothari, S. P.; and Shanken, J.. “Problems in Measuring Portfolio Performance: An Application to Contrarian Investment Strategies.” Journal of Financial Economics, 38 (1995), 79–107.CrossRefGoogle Scholar
Barber, B. M.; Odean, T.; and Zhu, N.. “Do Retail Trades Move Markets?” Review of Financial Studies, 22 (2008), 151–186.CrossRefGoogle Scholar
Barberis, N. “Psychology-Based Models of Asset Prices and Trading Volume.” In Handbook of Behavioral Economics – Foundations and Applications 1, Vol. 1, Bernheim, B. D., DellaVigna, S., and Laibson, D., eds. Amsterdam, Netherlands: Elsevier (2018), 79–175.Google Scholar
Bates, J. M., and Granger, C. W. J.. “The Combination of Forecasts.” Journal of the Operational Research Society, 20 (1969), 451–468.CrossRefGoogle Scholar
Brandt, M. W.; Santa-Clara, P.; and Valkanov, R.. “Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns.” Review of Financial Studies, 22 (2009), 3411–3447.CrossRefGoogle Scholar
Briere, M.; Lehalle, C.-A.; Nefedova, T.; and Raboun, A.. “Stock Market Liquidity and the Trading Costs of Asset Pricing Anomalies.” Available at SSRN 3380239 (2019).CrossRefGoogle Scholar
Campbell, J. Y., and Cochrane, J. H.. “By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior.” Journal of Political Economy, 107 (1999), 205–251.CrossRefGoogle Scholar
Chen, A. Y. “Most Claimed Statistical Findings in Cross-Sectional Return Predictability are Likely True.” Available at SSRN 3912915 (2021).CrossRefGoogle Scholar
Chen, A. Y., and Zimmermann, T.. “Publication Bias and the Cross-Section of Stock Returns.” Review of Asset Pricing Studies, 10 (2020), 249–289.CrossRefGoogle Scholar
Chen, A. Y., and Zimmermann, T.. “Open Source Cross Sectional Asset Pricing.” Critical Finance Review, 27 (2022), 207–264.CrossRefGoogle Scholar
Chordia, T.; Subrahmanyam, A.; and Tong, Q.. “Have Capital Market Anomalies Attenuated in the Recent Era of High Liquidity and Trading Activity?” Journal of Accounting and Economics, 58 (2014), 41–58.CrossRefGoogle Scholar
Cochrane, J. H. “Macro-Finance.” Review of Finance, 21 (2017), 945–985.CrossRefGoogle Scholar
Cohen, L.; Diether, K. B.; and Malloy, C. J.. “Supply and Demand Shifts in the Shorting Market.” Journal of Finance, 62 (2007), 2061–2096.CrossRefGoogle Scholar
Corwin, S. A., and Schultz, P.. “A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices.” Journal of Finance, 67 (2012), 719–760.CrossRefGoogle Scholar
Daniel, K., and Moskowitz, T. J.. “Momentum Crashes .” Journal of Financial Economics, 122 (2016), 221–247.CrossRefGoogle Scholar
Dawid, A. P. “Selection Paradoxes of Bayesian Inference.” In Lecture Notes-Monograph Series (1994), 211–220.CrossRefGoogle Scholar
DeMiguel, V.; Garlappi, L.; and Uppal, R.. “Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?” Review of Financial Studies, 22 (2009), 1915–1953.CrossRefGoogle Scholar
DeMiguel, V.; Martin-Utrera, A.; Nogales, F. J.; and Uppal, R.. “A Transaction-Cost Perspective on the Multitude of Firm Characteristics.” Review of Financial Studies, 33 (2020), 2180–2222.CrossRefGoogle Scholar
Detzel, A. L.; Novy-Marx, R.; and Velikov, M.. “Model Selection with Transaction Costs.” Available at SSRN 3805379 (2021).CrossRefGoogle Scholar
Drechsler, I., and Drechsler, Q. F.. “The Shorting Premium and Asset Pricing Anomalies” (2016).Google Scholar
Efron, B. “Tweedie’s Formula and Selection Bias.” Journal of the American Statistical Association, 106 (2011), 1602–1614.CrossRefGoogle ScholarPubMed
Efron, B. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction, Vol. 1. Cambridge: Cambridge University Press (2012).Google Scholar
Fama, E. F., and French, K. R.. “The Cross-Section of Expected Stock Returns.” Journal of Finance (1992), 427–465.CrossRefGoogle Scholar
Fong, K.; Holden, C.; and Tobek, O.. “Are Volatility Over Volume Liquidity Proxies Useful For Global Or US Research?” (2017).CrossRefGoogle Scholar
Frazzini, A.; Israel, R.; and Moskowitz, T. J.. “Trading Costs of Asset Pricing Anomalies” (2015).Google Scholar
Freyberger, J.; Neuhierl, A.; and Weber, M.. “Dissecting Characteristics Nonparametrically.” Review of Financial Studies, 33 (2020), 2326–2377.CrossRefGoogle Scholar
Gârleanu, N., and Pedersen, L. H.. “Efficiently Inefficient Markets for Assets and Asset Management.” Journal of Finance, 73 (2018), 1663–1712.CrossRefGoogle Scholar
Green, J.; Hand, J. R. M.; and Zhang, X. F.. “The Supraview of Return Predictive Signals.” Review of Accounting Studies, 18 (2013), 692–730.CrossRefGoogle Scholar
Green, J.; Hand, J. R. M.; and Zhang, X. F.. “The Characteristics that Provide Independent Information about Average us Monthly Stock Returns.” The Review of Financial Studies, 30 (2017), 4389–4436.CrossRefGoogle Scholar
Grossman, S. J., and Stiglitz, J. E.. “On the Impossibility of Informationally Efficient Markets .” American Economic Review, 70 (1980), 393–408.Google Scholar
Hand, J. R. M., and Green, J.. “The Importance of Accounting Information in Portfolio Optimization.” Journal of Accounting, Auditing & Finance, 26 (2011), 1–34.CrossRefGoogle Scholar
Hanna, J. D., and Ready, M. J.. “Profitable Predictability in the Cross Section of Stock Returns.” Journal of Financial Economics, 78 (2005), 463–505.CrossRefGoogle Scholar
Harvey, C. R.; Liu, Y.; and Zhu, H.. “… and the Cross-Section of Expected Returns.” Review of Financial Studies, 29 (2016), 5–68.CrossRefGoogle Scholar
Hasbrouck, J. “Trading Costs and Returns for US Equities: Estimating Effective Costs from Daily Data.” Journal of Finance, 64 (2009), 1445–1477.CrossRefGoogle Scholar
Holden, C. W., and Jacobsen, S.. “Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions.” Journal of Finance, 69 (2014), 1747–1785.CrossRefGoogle Scholar
Hou, K.; Kim, S.; and Werner, I. M.. “(Priced) Frictions” (2016).CrossRefGoogle Scholar
Hou, K.; Xue, C.; and Zhang, L.. “Replicating Anomalies.” Review of Financial Studies, 33 (2020), 2019–2133.CrossRefGoogle Scholar
Huang, J.-Z., and Huang, Z. J.. “Real-Time Profitability of Published Anomalies: An Out-of-Sample Test.” Quarterly Journal of Finance, 3 (2013), 03n04.CrossRefGoogle Scholar
Jacobs, H., and Müller, S.. “Anomalies Across the Globe: Once Public, No Longer Existent?” Journal of Financial Economics, 135 (2020), 213–230.CrossRefGoogle Scholar
Jahan-Parvar, M., and Zikes, F.. “When Do Low-Frequency Measures Really Measure Transaction Costs?” (2019).CrossRefGoogle Scholar
James, W., and Stein, C.. “Estimation with Quadratic Loss .” Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1 (1961), 361–379.Google Scholar
James, G.; Witten, D.; Hastie, T.; and Tibshirani, R.. An Introduction to Statistical Learning, Vol. 112. New York, NY: Springer (2013).CrossRefGoogle Scholar
Karnaukh, N.; Ranaldo, A.; and Soderlind, P.. “Understanding FX Liquidity.” Review of Financial Studies 28 (2015), 3073–3108.CrossRefGoogle Scholar
Kelly, B. T.; Pruitt, S.; and Su, Y.. “Characteristics Are Covariances: A Unified Model of Risk and Return.” Journal of Financial Economics, 134 (2019), 501–524.CrossRefGoogle Scholar
Knez, P. J., and Ready, M. J.. “Estimating the Profits from Trading Strategies.” Review of Financial Studies, 9 (1996), 1121–1163.CrossRefGoogle Scholar
Koch, A.; Ruenzi, S.; and Starks, L.. “Commonality in Liquidity: A Demand-Side Explanation.” Review of Financial Studies, 29 (2016), 1943–1974.CrossRefGoogle Scholar
Korajczyk, R. A., and Sadka, R.. “Are Momentum Profits Robust to Trading Costs?” Journal of Finance, 59 (2004), 1039–1082.CrossRefGoogle Scholar
Kyle, A. S., and Obizhaeva, A. A. “Market Microstructure Invariance: Empirical Hypotheses.” Econometrica, 84 (2016), 1345–1404.CrossRefGoogle Scholar
Lesmond, D. A.; Schill, M. J.; and Zhou, C.. “The Illusory Nature of Momentum Profits .” Journal of Financial Economics, 71 (2004), 349–380.CrossRefGoogle Scholar
Liu, L.; Moon, H. R.; and Schorfheide, F.. “Forecasting with Dynamic Panel Data Models.” Econometrica, 88 (2020), 171–201.CrossRefGoogle Scholar
Lo, A. W. “The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective.” Journal of Portfolio Management, 30 (2004), 15–29.CrossRefGoogle Scholar
Marquering, W.; Nisser, J.; and Valla, T.. “Disappearing Anomalies: A Dynamic Analysis of the Persistence of Anomalies.” Applied Financial Economics, 16 (2006), 291–302.CrossRefGoogle Scholar
McLean, R. D. “Idiosyncratic Risk, Long-Term Reversal, and Momentum.” Journal of Financial and Quantitative Analysis, 45 (2010), 883–906.CrossRefGoogle Scholar
McLean, R. D., and Pontiff, J.. “Does Academic Research Destroy Stock Return Predictability?” Journal of Finance, 71 (2016), 5–32.CrossRefGoogle Scholar
Novy-Marx, R., and Velikov, M.. “A Taxonomy of Anomalies and Their Trading Costs.” Review of Financial Studies, 29 (2016), 104–147.CrossRefGoogle Scholar
Novy-Marx, R., and Velikov, M.. “Comparing Cost-Mitigation Techniques.” Financial Analysts Journal, 75 (2019), 85–102.CrossRefGoogle Scholar
Patton, A. J., and Weller, B. M.. “What You See Is Not What You Get: The Costs of Trading Market Anomalies.” Journal of Financial Economics, 137 (2020), 515–549.CrossRefGoogle Scholar
Perold, A. F. “The Implementation Shortfall: Paper Versus Reality.” Journal of Portfolio Management, 14 (1988), 4.CrossRefGoogle Scholar
Pontiff, J., and Schill, M.. “Long-Run Seasoned Equity Offering Returns: Data Snooping, Model Misspecification, or Mispricing? A Costly Arbitrage Approach” (2001).Google Scholar
Rapach, D. E.; Strauss, J. K.; and Zhou, G.. “Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy.” Review of Financial Studies, 23 (2010), 821–862.CrossRefGoogle Scholar
Rapach, D. E., and Zhou, G.. “Forecasting Stock Returns.” In Handbook of Economic Forecasting, Vol 2, Timmerman, A and Elliott, G., eds. Amsterdam, Netherlands: Elsevier (2013), 328–383.Google Scholar
Roll, R. “A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market .” Journal of Finance, 39 (1984), 1127–1139.CrossRefGoogle Scholar
Schultz, P. “Transaction Costs and the Small Firm Effect: A Comment.” Journal of Financial Economics, 12 (1983), 81–88.CrossRefGoogle Scholar
Schwert, G. W. “Anomalies and Market Efficiency.” In Handbook of the Economics of Finance, Vol. 1, Constantinides, G., Harris, M., and Stulz, R., eds. Amsterdam, Netherlands: Elsevier (2003), 939–974.Google Scholar
Senn, S. “A Note Concerning a Selection “Paradox” of Dawid’s.” American Statistician, 62 (2008), 206–210.CrossRefGoogle Scholar
Stigler, S. M. “The 1988 Neyman Memorial Lecture: A Galtonian Perspective on Shrinkage Estimators.” Statistical Science, 5 (1990), 147–155.CrossRefGoogle Scholar
Stoll, H. R. “Market Microstructure.” In Handbook of the Economics of Finance, Vol. 1, Constantinides, G. M., Harris, M., and Stulzv, R. M., eds. New York, NY: Elsevier (2003), 553–604.Google Scholar
Stoll, H. R., and Whaley, R. E.. “Transaction Costs and the Small Firm Effect.” Journal of Financial Economics, 12 (1983), 57–79.CrossRefGoogle Scholar
Timmermann, A. “Forecast Combinations.” In Handbook of Economic Forecasting, Vol. 1, Elliott, G., Granger, C., and Timmermann, A., eds. New York, NY: Elsevier (2006), 135–196.CrossRefGoogle Scholar
Wooldridge, J. M. “Estimation and Inference for Dependent Processes.” In Handbook of Econometrics, Vol. 4, R. Engle and D. McFadden, eds. New York, NY: Elsevier (1994), 2639–2738.Google Scholar
WRDS: Center for Research in Security Prices. “CRSP/Compustat Merged Database.” Wharton Research Data Services, http://www.whartonwrds.com/datasets/crsp/.Google Scholar
Xie, X.; Kou, S. C.; and Brown, L. D.. “SURE Estimates for a Heteroscedastic Hierarchical Model.” Journal of the American Statistical Association, 107 (2012), 1465–1479.CrossRefGoogle ScholarPubMed