Latent factor model for asset pricing

Ajim Uddin and Dantong Yu

Journal of Behavioral and Experimental Finance, 2020, vol. 27, issue C

Abstract: One of the fundamental questions in asset pricing is ‘Why different assets earn different average returns?’ In this paper, we designed an autoencoder based asset pricing model to explain the return difference among the stocks in an index. The trained autoencoder generates a set of latent representations that constitutes a combined -‘communal’- factor to better explains a large portion of the return differences among the stocks in an index. After analyzing all the stocks in S&P-500, Russel-3000, and NASDAQ-100, we found that our proposed latent factor model outperforms many other factor models in predicting the next day’s return. Notably, the experiment results show that on average non-communal stocks earn 0.05% over communal stocks. However, the risk associated with this non-communal stock is also 0.8% higher than communal stocks. The experiments confirm that the superior performance comes from the compensation of high risk associated with these non-communal stocks. Investors will benefit from our latent factor model to identify these communal and non-communal stocks for a high return while diversifying their asset portfolio.

Keywords: Asset pricing; Nonlinear factor model; Machine learning; Autoencoders; Fintech (search for similar items in EconPapers)
JEL-codes: C38 C53 G11 G12 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214635019302333

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:eee:beexfi:v:27:y:2020:i:c:s2214635019302333

DOI: 10.1016/j.jbef.2020.100353

Access Statistics for this article

Journal of Behavioral and Experimental Finance is currently edited by Michael Dowling and Jürgen Huber

More articles in Journal of Behavioral and Experimental Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().