Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?

Huei-Wen Teng () and Yu-Hsien Li ()
Additional contact information
Huei-Wen Teng: National Yang Ming Chiao Tung University
Yu-Hsien Li: Taishin International Bank

Digital Finance, 2023, vol. 5, issue 1, No 7, 149-182

Abstract: Abstract In asset pricing, most studies focus on finding new factors, such as macroeconomic factors or firm characteristics, to explain risk premiums. Investigating whether these factors help forecast stock returns remains active research in finance and computer science. This paper conducts an extensive comparative analysis using a large set of pricing factors. It compares out-of-sample stock-level and portfolio-level prediction performance among neural networks, the traditional Fama-MacBeth regression, and other supervised learning algorithms such as regression and tree-based algorithms. Our analysis shows the benefit of employing neural networks, and deeper neural networks enjoy marginal improvements in terms of prediction.

Keywords: Asset pricing; Fama-MacBeth regression; Elastic net; Regression tree; Boosting; Neural network (search for similar items in EconPapers)
JEL-codes: C45 C57 C58 G12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42521-023-00076-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:digfin:v:5:y:2023:i:1:d:10.1007_s42521-023-00076-y

Ordering information: This journal article can be ordered from
https://www.springer.com/finance/journal/42521

DOI: 10.1007/s42521-023-00076-y

Access Statistics for this article

Digital Finance is currently edited by Wolfgang Karl Härdle, Steven Kou and Min Dai

More articles in Digital Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().