Next Article in Journal
Efficient Retirement Portfolios: Using Life Insurance to Meet Income and Bequest Goals in Retirement
Previous Article in Journal
Surplus Sharing with Coherent Utility Functions
Open AccessArticle

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
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
Show Figures

Figure 1

MDPI and ACS Style

De Giuli, M.E.; Greppi, A.; Resta, M. An Object-Oriented Bayesian Framework for the Detection of Market Drivers. Risks 2019, 7, 8.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop