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

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...

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Main Authors:	José María Sarabia, Faustino Prieto, Vanesa Jordá, Stefan Sperlich
Format:	Article
Language:	English
Published:	MDPI AG 2020-04-01
Series:	Risks
Subjects:	semiparametric modeling machine learning VaR estimation analyzing financial data
Online Access:	https://www.mdpi.com/2227-9091/8/2/32

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spelling	doaj-68ddd929db6945088426e8fd3c4cf1272020-11-25T02:10:45ZengMDPI AGRisks2227-90912020-04-018323210.3390/risks8020032A Note on Combining Machine Learning with Statistical Modeling for Financial Data AnalysisJosé María Sarabia0Faustino Prieto1Vanesa Jordá2Stefan Sperlich3Department of Economics, University of Cantabria, 39005 Santander, SpainDepartment of Economics, University of Cantabria, 39005 Santander, SpainDepartment of Economics, University of Cantabria, 39005 Santander, SpainGeneva School of Economics and Management, University of Geneva, 1211 Geneva, SwitzerlandThis 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.https://www.mdpi.com/2227-9091/8/2/32semiparametric modelingmachine learningVaR estimationanalyzing financial data
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	José María Sarabia Faustino Prieto Vanesa Jordá Stefan Sperlich
spellingShingle	José María Sarabia Faustino Prieto Vanesa Jordá Stefan Sperlich A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis Risks semiparametric modeling machine learning VaR estimation analyzing financial data
author_facet	José María Sarabia Faustino Prieto Vanesa Jordá Stefan Sperlich
author_sort	José María Sarabia
title	A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis
title_short	A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis
title_full	A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis
title_fullStr	A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis
title_full_unstemmed	A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis
title_sort	note on combining machine learning with statistical modeling for financial data analysis
publisher	MDPI AG
series	Risks
issn	2227-9091
publishDate	2020-04-01
description	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.
topic	semiparametric modeling machine learning VaR estimation analyzing financial data
url	https://www.mdpi.com/2227-9091/8/2/32
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A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis

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