Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data
Marchese, M. 
ORCID: 0000-0001-6801-911X, Martinez-Miranda, M. D., Nielsen, J. P. ORCID: 0000-0001-6874-1268 & Scholz, M. (2024).
Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data.
Financial Innovation, 10(1),
article number 138.
doi: 10.1186/s40854-024-00657-9
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. Practical applications to annual financial returns and actuarial telematics data show its usefulness in the financial and insurance industries.
| Publication Type: | Article |
|---|---|
| Publisher Keywords: | forecasting, non-linear prediction, stock returns, dimension reduction, telematics |
| Subjects: | H Social Sciences > HF Commerce H Social Sciences > HG Finance Q Science > QA Mathematics |
| Departments: | Bayes Business School Bayes Business School > Actuarial Science & Insurance |
| SWORD Depositor: |
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