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Network Structure Of Financial Market Of S&P 500 Around Financial Crises
Subject area: Science,Engineering and Technology · Area of research: Computer Science
Abstract
The structural change of financial network of S&P 500 around financial crises from 1998-2012 with 6-months? time window is investigated. We construct a planar maximally filtered graph from correlations between companies. We calculate the average shortest path and clustering coefficient of the financial networks to observe the change of the network. We found the higher average shortest path before the crises and decreases over time until market enter calm state. However, the average clustering coefficient decreases in the beginning of the crises and increases with the intense of crises. The change of network structure can identify financial states which can be useful for portfolio investment.
Keywords
Correlation network, financial network, network properties, planar maximum filtered graph
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How to cite this paper
@article{1701416,
author = {Sree Narayan Chakraborty, Md. Javed Hossain, Ashadun Nobi, Mohammed Nizam Uddin},
title = {Network Structure Of Financial Market Of S&P 500 Around Financial Crises},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {1},
pages = {229-233},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1701416.pdf},
abstract = {The structural change of financial network of S&P 500 around financial crises from 1998-2012 with 6-months? time window is investigated. We construct a planar maximally filtered graph from correlations between companies. We calculate the average shortest path and clustering coefficient of the financial networks to observe the change of the network. We found the higher average shortest path before the crises and decreases over time until market enter calm state. However, the average clustering coefficient decreases in the beginning of the crises and increases with the intense of crises. The change of network structure can identify financial states which can be useful for portfolio investment. },
keywords = {Correlation network, financial network, network properties, planar maximum filtered graph},
month = {July},
}