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

Autor:	Malvina Marchese, María Dolores Martínez-Miranda, Jens Perch Nielsen, Michael Scholz
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Forecasting Non-linear prediction Stock returns Dimension reduction Telematics Public finance K4430-4675 Finance HG1-9999
Zdroj:	Financial Innovation, Vol 10, Iss 1, Pp 1-16 (2024)
Druh dokumentu:	article
ISSN:	2199-4730
DOI:	10.1186/s40854-024-00657-9
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/958b8a3121934d70b61f5a0fc09e07dc Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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