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18 pages, 742 KiB  
Article
An Object-Oriented Bayesian Framework for the Detection of Market Drivers
by Maria Elena De Giuli, Alessandro Greppi and Marina Resta
Risks 2019, 7(1), 8; https://doi.org/10.3390/risks7010008 - 14 Jan 2019
Cited by 1 | Viewed by 3612
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 [...] Read more.
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. Full article
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