Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309510 
Year of Publication: 
2022
Citation: 
[Journal:] Review of Quantitative Finance and Accounting [ISSN:] 1573-7179 [Volume:] 60 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 195-230
Publisher: 
Springer US, New York, NY
Abstract: 
We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.
Subjects: 
Anomalies
Machine learning models
Efficient market hypothesis
Asset pricing models
JEL: 
G12
G29
M41
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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