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An Object-Oriented Bayesian Framework for the Detection of Market Drivers

Author

Listed:
  • Maria Elena De Giuli

    (Department of Economics and Management, University of Pavia, 27100 Pavia PV, Italy)

  • Alessandro Greppi

    (Zurich Investment Life, 20159 Milan MI, Italy)

  • Marina Resta

    (School of Social Sciences, Department of Economics and Business Studies, University of Genova, 16126 Genova GE, Italy)

Abstract

We use Object Oriented Bayesian Networks (OOBNs) to analyze complex ties in the equity market and to detect drivers for the Standard & Poor’s 500 (S&P 500) index. To such aim, we consider a vast number of indicators drawn from various investment areas (Value, Growth, Sentiment, Momentum, and Technical Analysis), and, with the aid of OOBNs, we study the role they played along time in influencing the dynamics of the S&P 500. Our results highlight that the centrality of the indicators varies in time, and offer a starting point for further inquiries devoted to combine OOBNs with trading platforms.

Suggested Citation

  • Maria Elena De Giuli & Alessandro Greppi & Marina Resta, 2019. "An Object-Oriented Bayesian Framework for the Detection of Market Drivers," Risks, MDPI, vol. 7(1), pages 1-18, January.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:8-:d:197533
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    References listed on IDEAS

    as
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    Keywords

    OOBN; Market Drivers; S&P 500;
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