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

Author

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  • José María Sarabia

    (Department of Economics, University of Cantabria, 39005 Santander, Spain)

  • Faustino Prieto

    (Department of Economics, University of Cantabria, 39005 Santander, Spain)

  • Vanesa Jordá

    (Department of Economics, University of Cantabria, 39005 Santander, Spain)

  • Stefan Sperlich

    (Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland)

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.

Suggested Citation

  • José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:2:p:32-:d:341113
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    References listed on IDEAS

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    Cited by:

    1. Vali Asimit & Ioannis Kyriakou & Jens Perch Nielsen, 2020. "Special Issue “Machine Learning in Insurance”," Risks, MDPI, vol. 8(2), pages 1-2, May.

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