Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/313665 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Asset Management [ISSN:] 1479-179X [Volume:] 24 [Issue:] 5 [Publisher:] Palgrave Macmillan [Place:] London [Year:] 2023 [Pages:] 419-441
Publisher: 
Palgrave Macmillan, London
Abstract: 
We identify the characteristics and specifications that drive the out-of-sample performance of machine-learning models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant monthly value-weighted (long-short) returns of around 1.8–2.2%, and a vast majority of tested models outperform a linear combination of predictors (our baseline factor benchmark) by a substantial margin. Composite predictors based on machine learning have long-short portfolio returns that remain significant even with transaction costs up to 300 basis points. By comparing 46 variations of machine-learning models, we find that the models with the highest return predictability apply a feed-forward neural network or composite predictors, with extending rolling windows, including elastic net as a feature reduction, and using percent ranked returns as a target. The results of our nonlinear models are significant across several classical asset pricing models and uncover market inefficiencies that challenge current asset pricing theories in international markets.
Subjects: 
International stock market
Anomalies
Machines learning models
Market efficiency
Publication impact
JEL: 
G12
G29
M41
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.