10 Large Scale Factor Anomaly Studies with Definitions

  1. A Taxonomy of Anomalies and their Trading Costs 2015, Robert Novy-Marx and Mihail Velikov (with data)

  2. …and the Cross-Section of Expected Returns, 2013, Campbell Harvey, Yan Liu, Caroline Zhu (factor list)

  3. A Comparison of New Factor Models, 2014, Kewei Hou, Chen Xue, Lu Zhang

  4. The Supraview of Return Predictive Signals, 2012, Jeremiah Green, John Hand, Frank Zhang

  5. Does Academic Research Destroy Stock Return Predictability? 2012, David McLean, Jeffrey Pontiff

  6. Shrinking the Cross Section, 2017, Serhiy Kozak, Stefan Nagel

  7. Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach, 2017, Xuemin (Sterling) Yan, Lingling Zheng

  8. p-Hacking: Evidence from Two Million Trading Strategies, 2017, Tarun Chordia, Amit Goyal, Alessio Saretto

  9. Taming the Factor Zoo: A Test of New Factors, 2017, Guanhao Feng, Stefano Giglio, Dacheng Xiu

  10. Empirical Asset Pricing via Machine Learning, 2019, Shihao Gu, Bryan T. Kelly, Dacheng Xiu

Post blog publication additions:

  1. Time Series Variation in the Factor Zoo, 2022, Bessembinder, Burt, Hrdlicka

  2. Enhancing Stock Market Anomalies with Machine Learning, 2021, Azevedo, Vitor, Hoegner, Christopher

  3. Open Source Cross-Sectional Asset Pricing, 2020, Chen, Andrew, Zimmermann, Tom

  4. The World of Anomalies: Smaller Than We Think?, 2020, Hollstein, Fabian

  5. Zeroing in on the Expected Returns of Anomalies 2020, Chen Andrew, Velikov, Mihail

  6. Anomalies across the globe: Once public, no longer existent? 2020, Jacobs, Heiko, Müllerb, Sebastian

  7. Does it pay to follow anomalies research? Machine learning approach with international evidence? 2020, Tobek, Ondrej, Hronec, Martin

  8. ...And Nothing Else Matters? On the Dimensionality and Predictability of International Stock Returns, 2018, Jacobs, Heiko, Müllerb, Sebastian

  9. Replicating Anomalies 2017, Hou, Xue, Zhang

PS.

Please share new ones here @msamonov

thanks @AdamZaremba and @RyanPKirlin for sending over some great ones I missed.