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A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis

Abstract: This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit modeling with purely data adaptive inference. Then, we discuss stepwise approaches with different ways of integrating machine learning. Furthermore, for the modeling of prior knowledge, we introduce classes of distribution families for financial data. The proposed procedures are illustrated with data on stock returns for five companies of the Spanish value-weighted index IBEX35.

 Autoría: Sarabia J.M., Prieto F., Jordá V., Sperlich S.,

 Fuente: Risks, Volume 8, Issue 2, June 2020, Article number 32

 Editorial: Multidisciplinary Digital Publishing Institute (MDPI)

 Fecha de publicación: 01/06/2020

 Nº de páginas: 14

 Tipo de publicación: Artículo de Revista

 DOI: 10.3390/risks8020032

 ISSN: 2227-9091

 Proyecto español: ECO2016-76203-C2-1-P

 Url de la publicación: https://doi.org/10.3390/risks8020032