Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257846 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 1 [Article No.:] 8 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-18
Publisher: 
MDPI, Basel
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.
Subjects: 
OOBN
Market Drivers
S&P 500
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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