Structure and dynamics of financial networks by feature ranking method

Abstract Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do s...

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Autores principales:	Mahmudul Islam Rakib, Ashadun Nobi, Jae Woo Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74cf3bcd62a942bdbc75ff876706e072
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spelling	oai:doaj.org-article:74cf3bcd62a942bdbc75ff876706e0722021-12-02T19:04:36ZStructure and dynamics of financial networks by feature ranking method10.1038/s41598-021-97100-12045-2322https://doaj.org/article/74cf3bcd62a942bdbc75ff876706e0722021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97100-1https://doaj.org/toc/2045-2322Abstract Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.Mahmudul Islam RakibAshadun NobiJae Woo LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mahmudul Islam Rakib Ashadun Nobi Jae Woo Lee Structure and dynamics of financial networks by feature ranking method
description	Abstract Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.
format	article
author	Mahmudul Islam Rakib Ashadun Nobi Jae Woo Lee
author_facet	Mahmudul Islam Rakib Ashadun Nobi Jae Woo Lee
author_sort	Mahmudul Islam Rakib
title	Structure and dynamics of financial networks by feature ranking method
title_short	Structure and dynamics of financial networks by feature ranking method
title_full	Structure and dynamics of financial networks by feature ranking method
title_fullStr	Structure and dynamics of financial networks by feature ranking method
title_full_unstemmed	Structure and dynamics of financial networks by feature ranking method
title_sort	structure and dynamics of financial networks by feature ranking method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/74cf3bcd62a942bdbc75ff876706e072
work_keys_str_mv	AT mahmudulislamrakib structureanddynamicsoffinancialnetworksbyfeaturerankingmethod AT ashadunnobi structureanddynamicsoffinancialnetworksbyfeaturerankingmethod AT jaewoolee structureanddynamicsoffinancialnetworksbyfeaturerankingmethod
_version_	1718377217282539520

Structure and dynamics of financial networks by feature ranking method

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