Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data

Malvina Marchese (), María Dolores Martínez-Miranda (), Jens Perch Nielsen () and Michael Scholz ()
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Malvina Marchese: City, University of London
María Dolores Martínez-Miranda: University of Granada
Jens Perch Nielsen: City, University of London
Michael Scholz: University of Klagenfurt

Financial Innovation, 2024, vol. 10, issue 1, 1-16

Abstract: Abstract The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations. Empirical applications to annual financial returns and actuarial telematics data show its usefulness in the financial and insurance industries.

Keywords: Forecasting; Non-linear prediction; Stock returns; Dimension reduction; Telematics (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 G17 G22 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1186/s40854-024-00657-9

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