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

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.

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Year of publication:	2024
Authors:	Marchese, Malvina ; Martinez Miranda, Maria Dolores ; Nielsen, Jens Perch ; Scholz, Michael
Published in:	Financial innovation : FIN. - Heidelberg : SpringerOpen, ISSN 2199-4730, ZDB-ID 2824759-0. - Vol. 10.2024, Art.-No. 138, p. 1-16
Subject:	Forecasting \| Non‑linear prediction \| Stock returns \| Dimension reduction \| Telematics \| Prognoseverfahren \| Forecasting model \| Kapitaleinkommen \| Capital income \| Regressionsanalyse \| Regression analysis \| Börsenkurs \| Share price \| Telekommunikation \| Telecommunications