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

1
Department of Economics and Management, University of Pavia, 27100 Pavia PV, Italy
2
Zurich Investment Life, 20159 Milan MI, Italy
3
School of Social Sciences, Department of Economics and Business Studies, University of Genova, 16126 Genova GE, Italy
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 3 January 2019 / Accepted: 4 January 2019 / Published: 14 January 2019
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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. View Full-Text
Keywords: OOBN; Market Drivers; S&P 500 OOBN; Market Drivers; S&P 500
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De Giuli, M.E.; Greppi, A.; Resta, M. An Object-Oriented Bayesian Framework for the Detection of Market Drivers. Risks 2019, 7, 8.

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