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Baltas, Konstantinos, Fruet Dias, Gustavo 
ORCID: https://orcid.org/0000-0001-6428-5074 and Rumiancev, Artiom
(2026)
A Regularized Instrumental Principal Components Analysis.
Quantitative Finance.
ISSN 1469-7688
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Abstract
This paper examines instrumented principal components analysis (IPCA), one of the newly developed machine learning methods to model asset returns. It proposes the implementation of a support vector regression (SVR) into IPCA to better capture the relationship between firm characteristics and returns. Using a set of 9,689 equities and 46 characteristics we show that IPCA(SVR) has better out-of-sample performance compared to the classic IPCA and multi-factor models in the majority of cases. The results imply that the least squares approach of IPCA overfits the characteristic sensitivity matrix, resulting in better in-sample results but worse out-of-sample fit than regularized IPCA. Although the regularized specifications exhibit superior predictive performance, the null hypothesis of no pricing errors is rejected in several testing environments, highlighting the distinction between predictive accuracy and asset-pricing validity.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ipca,support vector regression,asset pricing,machine learning,das to be completed ,/dt/atira/pure/keyword/DAStatement/das_to_be_completed |
| Faculty \ School: | Faculty of Social Sciences > School of Economics |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance Faculty of Science > Research Groups > Statistics |
| Depositing User: | LivePure Connector |
| Date Deposited: | 06 Nov 2026 11:50 |
| Last Modified: | 08 Nov 2026 00:06 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104999 |
| DOI: | 10.1080/14697688.2026.2740081 |
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