Robustifying and simplifying high‑dimensional regression with applications to yearly stock return and telematics data Marchese, Malvina Martínez Miranda, María Dolores Perch Nielsen, Jens Scholz, Michael Forecasting Non-linear prediction Stock returns 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. 2024-10-16T07:50:16Z 2024-10-16T07:50:16Z 2024-10-02 journal article Marchese, M. et. al. Financial Innovation (2024) 10:138. [https://doi.org/10.1186/s40854-024-00657-9] https://hdl.handle.net/10481/95987 10.1186/s40854-024-00657-9 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer