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Open AccessArticle

Transfer Entropy between Communities in Complex Financial Networks

by Jan Korbel 1,2,3,*, Xiongfei Jiang 4 and Bo Zheng 5,6
1
Section for Science of Complex Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
2
Complexity Science Hub Vienna, Josefstädterstrasse 39, 1080 Vienna, Austria
3
Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic
4
College of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315175, China
5
Department of Physics, Zhejiang University, Hangzhou 310027, China
6
School of Information Engineering, Ningbo Dahongying University, Ningbo 315175, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1124; https://doi.org/10.3390/e21111124
Received: 23 October 2019 / Revised: 11 November 2019 / Accepted: 14 November 2019 / Published: 15 November 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
In this paper, we analyze information flows between communities of financial markets, represented as complex networks. Each community, typically corresponding to a business sector, represents a significant part of the financial market and the detection of interactions between communities is crucial in the analysis of risk spreading in the financial markets. We show that the transfer entropy provides a coherent description of information flows in and between communities, also capturing non-linear interactions. Particularly, we focus on information transfer of rare events—typically large drops which can spread in the network. These events can be analyzed by Rényi transfer entropy, which enables to accentuate particular types of events. We analyze transfer entropies between communities of the five largest financial markets and compare the information flows with the correlation network of each market. From the transfer entropy picture, we can also identify the non-linear interactions, which are typical in the case of extreme events. The strongest flows can be typically observed between specific types of business sectors—financial sectors is the most significant example. View Full-Text
Keywords: financial networks; community structure; information transfer; Rényi entropy financial networks; community structure; information transfer; Rényi entropy
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Korbel, J.; Jiang, X.; Zheng, B. Transfer Entropy between Communities in Complex Financial Networks. Entropy 2019, 21, 1124.

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