Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/326965 
Year of Publication: 
2025
Citation: 
[Journal:] Junior Management Science (JUMS) [ISSN:] 2942-1861 [Volume:] 10 [Issue:] 3 [Year:] 2025 [Pages:] 561-581
Publisher: 
Junior Management Science e. V., Planegg
Abstract: 
Recent studies highlight the superior performance of non-linear machine learning models, such as neural networks, over traditional linear models in predicting cross-sectional stock returns. These models are capable of capturing complex non-linear interactions between predictive signals and future returns. This thesis researches whether sector-specific neural networks can detect sector-related relationships to outperform a global neural network. It evaluates the predictive power of these models at the stock level and in portfolios based on return forecasts, constructing long-short portfolios from the networks' sorted predictions. A global neural network model trained on the full sample of stocks dominates neural networks trained on individual GICS sectors in predicting the cross-section of US stock returns. Sector-specific neural networks fail to gain an advantage by capturing complex sector-specific interactions. They underperform the global neural network especially in the early out-of-sample period. The smaller sample size for each GICS sector requires a trade-off between model complexity and robust model estimation. Pooling the data for the global model solves this problem and supports the predictive power of neural networks for stock returns.
Subjects: 
cross-section of stock returns
machine learning
neural networks
return prediction
sector models
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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