Measuring the systemic importance of interconnected industries in the world economic system
Purpose The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries. Design/methodology/approach First, the authors i...
Gespeichert in:
Veröffentlicht in: | Industrial management + data systems 2017-01, Vol.117 (1), p.110-130 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 130 |
---|---|
container_issue | 1 |
container_start_page | 110 |
container_title | Industrial management + data systems |
container_volume | 117 |
creator | Cheng, Xian Shaoyi, Liao Stephen Hua, Zhongsheng |
description | Purpose
The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.
Design/methodology/approach
First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.
Findings
The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.
Practical implications
The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.
Originality/value
Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network. |
doi_str_mv | 10.1108/IMDS-10-2015-0442 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1858069291</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4300350721</sourcerecordid><originalsourceid>FETCH-LOGICAL-c314t-3838a32a517d2136d035a9f2c6cf5f43e28c6543c9ccd56ef1cf13ca544e11e63</originalsourceid><addsrcrecordid>eNptkE1LxDAQhoMouK7-AG8Fz9FM0qTpUVZdF3bxoJ48lJBOtMu2WZMU2X9va70InuaD95mBh5BLYNcATN-sNnfPFBjlDCRlec6PyAwKqakstDgmM8aVoFIWxSk5i3HL2LDgakbeNmhiH5ruPUsfmMVDTNg2NmvavQ_JdBYz77KmSxis7zq0CethrPuYQoNxaH-4Lx92dYZDxI_0dOacnDizi3jxW-fk9eH-ZfFI10_L1eJ2Ta2APFGhhTaCGwlFzUGomglpSsetsk66XCDXVslc2NLaWip0YB0Ia2SeIwAqMSdX09198J89xlRtfR-64WUFWmqmSl7CkIIpZYOPMaCr9qFpTThUwKrRYTU6HIfRYTU6HBg2MdhiMLv6X-SPdvENFf90Jw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1858069291</pqid></control><display><type>article</type><title>Measuring the systemic importance of interconnected industries in the world economic system</title><source>Emerald A-Z Current Journals</source><source>Standard: Emerald eJournal Premier Collection</source><creator>Cheng, Xian ; Shaoyi, Liao Stephen ; Hua, Zhongsheng</creator><creatorcontrib>Cheng, Xian ; Shaoyi, Liao Stephen ; Hua, Zhongsheng</creatorcontrib><description>Purpose
The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.
Design/methodology/approach
First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.
Findings
The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.
Practical implications
The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.
Originality/value
Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.</description><identifier>ISSN: 0263-5577</identifier><identifier>EISSN: 1758-5783</identifier><identifier>DOI: 10.1108/IMDS-10-2015-0442</identifier><language>eng</language><publisher>Wembley: Emerald Publishing Limited</publisher><subject>Algorithms ; Banking industry ; Business ; Connectivity ; Datasets ; Economic crisis ; Economics ; Financial institutions ; Global economy ; Grants ; Information management ; Intelligence (information) ; International trade ; Investigations ; Markets ; Regulation of financial institutions ; Risk ; Risk management</subject><ispartof>Industrial management + data systems, 2017-01, Vol.117 (1), p.110-130</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c314t-3838a32a517d2136d035a9f2c6cf5f43e28c6543c9ccd56ef1cf13ca544e11e63</citedby><cites>FETCH-LOGICAL-c314t-3838a32a517d2136d035a9f2c6cf5f43e28c6543c9ccd56ef1cf13ca544e11e63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/IMDS-10-2015-0442/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,967,11635,21695,27924,27925,52689,53244</link.rule.ids></links><search><creatorcontrib>Cheng, Xian</creatorcontrib><creatorcontrib>Shaoyi, Liao Stephen</creatorcontrib><creatorcontrib>Hua, Zhongsheng</creatorcontrib><title>Measuring the systemic importance of interconnected industries in the world economic system</title><title>Industrial management + data systems</title><description>Purpose
The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.
Design/methodology/approach
First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.
Findings
The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.
Practical implications
The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.
Originality/value
Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.</description><subject>Algorithms</subject><subject>Banking industry</subject><subject>Business</subject><subject>Connectivity</subject><subject>Datasets</subject><subject>Economic crisis</subject><subject>Economics</subject><subject>Financial institutions</subject><subject>Global economy</subject><subject>Grants</subject><subject>Information management</subject><subject>Intelligence (information)</subject><subject>International trade</subject><subject>Investigations</subject><subject>Markets</subject><subject>Regulation of financial institutions</subject><subject>Risk</subject><subject>Risk management</subject><issn>0263-5577</issn><issn>1758-5783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNptkE1LxDAQhoMouK7-AG8Fz9FM0qTpUVZdF3bxoJ48lJBOtMu2WZMU2X9va70InuaD95mBh5BLYNcATN-sNnfPFBjlDCRlec6PyAwKqakstDgmM8aVoFIWxSk5i3HL2LDgakbeNmhiH5ruPUsfmMVDTNg2NmvavQ_JdBYz77KmSxis7zq0CethrPuYQoNxaH-4Lx92dYZDxI_0dOacnDizi3jxW-fk9eH-ZfFI10_L1eJ2Ta2APFGhhTaCGwlFzUGomglpSsetsk66XCDXVslc2NLaWip0YB0Ia2SeIwAqMSdX09198J89xlRtfR-64WUFWmqmSl7CkIIpZYOPMaCr9qFpTThUwKrRYTU6HIfRYTU6HBg2MdhiMLv6X-SPdvENFf90Jw</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Cheng, Xian</creator><creator>Shaoyi, Liao Stephen</creator><creator>Hua, Zhongsheng</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L.0</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20170101</creationdate><title>Measuring the systemic importance of interconnected industries in the world economic system</title><author>Cheng, Xian ; Shaoyi, Liao Stephen ; Hua, Zhongsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c314t-3838a32a517d2136d035a9f2c6cf5f43e28c6543c9ccd56ef1cf13ca544e11e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Banking industry</topic><topic>Business</topic><topic>Connectivity</topic><topic>Datasets</topic><topic>Economic crisis</topic><topic>Economics</topic><topic>Financial institutions</topic><topic>Global economy</topic><topic>Grants</topic><topic>Information management</topic><topic>Intelligence (information)</topic><topic>International trade</topic><topic>Investigations</topic><topic>Markets</topic><topic>Regulation of financial institutions</topic><topic>Risk</topic><topic>Risk management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cheng, Xian</creatorcontrib><creatorcontrib>Shaoyi, Liao Stephen</creatorcontrib><creatorcontrib>Hua, Zhongsheng</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Industrial management + data systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cheng, Xian</au><au>Shaoyi, Liao Stephen</au><au>Hua, Zhongsheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measuring the systemic importance of interconnected industries in the world economic system</atitle><jtitle>Industrial management + data systems</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>117</volume><issue>1</issue><spage>110</spage><epage>130</epage><pages>110-130</pages><issn>0263-5577</issn><eissn>1758-5783</eissn><abstract>Purpose
The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.
Design/methodology/approach
First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.
Findings
The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.
Practical implications
The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.
Originality/value
Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.</abstract><cop>Wembley</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/IMDS-10-2015-0442</doi><tpages>21</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0263-5577 |
ispartof | Industrial management + data systems, 2017-01, Vol.117 (1), p.110-130 |
issn | 0263-5577 1758-5783 |
language | eng |
recordid | cdi_proquest_journals_1858069291 |
source | Emerald A-Z Current Journals; Standard: Emerald eJournal Premier Collection |
subjects | Algorithms Banking industry Business Connectivity Datasets Economic crisis Economics Financial institutions Global economy Grants Information management Intelligence (information) International trade Investigations Markets Regulation of financial institutions Risk Risk management |
title | Measuring the systemic importance of interconnected industries in the world economic system |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T18%3A29%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Measuring%20the%20systemic%20importance%20of%20interconnected%20industries%20in%20the%20world%20economic%20system&rft.jtitle=Industrial%20management%20+%20data%20systems&rft.au=Cheng,%20Xian&rft.date=2017-01-01&rft.volume=117&rft.issue=1&rft.spage=110&rft.epage=130&rft.pages=110-130&rft.issn=0263-5577&rft.eissn=1758-5783&rft_id=info:doi/10.1108/IMDS-10-2015-0442&rft_dat=%3Cproquest_cross%3E4300350721%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1858069291&rft_id=info:pmid/&rfr_iscdi=true |