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Article

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

1
Department of Economics, University of Cantabria, 39005 Santander, Spain
2
Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
Risks 2020, 8(2), 32; https://doi.org/10.3390/risks8020032
Received: 19 December 2019 / Revised: 30 March 2020 / Accepted: 1 April 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Machine Learning in Insurance)
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. View Full-Text
Keywords: semiparametric modeling; machine learning; VaR estimation; analyzing financial data semiparametric modeling; machine learning; VaR estimation; analyzing financial data
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MDPI and ACS Style

Sarabia, J.M.; Prieto, F.; Jordá, V.; Sperlich, S. A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis. Risks 2020, 8, 32. https://doi.org/10.3390/risks8020032

AMA Style

Sarabia JM, Prieto F, Jordá V, Sperlich S. A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis. Risks. 2020; 8(2):32. https://doi.org/10.3390/risks8020032

Chicago/Turabian Style

Sarabia, José María, Faustino Prieto, Vanesa Jordá, and Stefan Sperlich. 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis" Risks 8, no. 2: 32. https://doi.org/10.3390/risks8020032

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