Discovering company revenue relations from news: A network approach

Zhongming Ma; Olivia R.L. Sheng; Gautam Pant

doi:10.1016/j.dss.2009.04.007

Discovering company revenue relations from news: A network approach

Zhongming Ma, Olivia R.L. Sheng, Gautam Pant

Research output: Contribution to journal › Article › peer-review

Abstract

Large volumes of online business news provide an opportunity to explore various aspects of companies. A news story pertaining to a company often cites other companies. Using such company citations we construct an intercompany network, employ social network analysis techniques to identify a set of attributes from the network structure, and feed the attributes to machine learning methods to predict the company revenue relation (CRR) that is based on two companies' relative quantitative financial data. Hence, we seek to understand the power of network structural attributes in predicting CRRs that are not described in the news or known at the time the news was published. The network attributes produce close to 80% precision, recall, and accuracy for all 87,340 company pairs in the network. This approach is scalable and can be extended to private and foreign companies for which financial data is unavailable or hard to procure.

Original language	English (US)
Pages (from-to)	408-414
Number of pages	7
Journal	Decision Support Systems
Volume	47
Issue number	4
DOIs	https://doi.org/10.1016/j.dss.2009.04.007
State	Published - Nov 2009
Externally published	Yes

Keywords

Business news
Intercompany network
Revenue comparison
Social network analysis
Web mining

ASJC Scopus subject areas

Management Information Systems
Information Systems
Developmental and Educational Psychology
Arts and Humanities (miscellaneous)
Information Systems and Management

Online availability

10.1016/j.dss.2009.04.007

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@article{794f33cf4cad4b158e70c0154fb67c25,

title = "Discovering company revenue relations from news: A network approach",

abstract = "Large volumes of online business news provide an opportunity to explore various aspects of companies. A news story pertaining to a company often cites other companies. Using such company citations we construct an intercompany network, employ social network analysis techniques to identify a set of attributes from the network structure, and feed the attributes to machine learning methods to predict the company revenue relation (CRR) that is based on two companies' relative quantitative financial data. Hence, we seek to understand the power of network structural attributes in predicting CRRs that are not described in the news or known at the time the news was published. The network attributes produce close to 80% precision, recall, and accuracy for all 87,340 company pairs in the network. This approach is scalable and can be extended to private and foreign companies for which financial data is unavailable or hard to procure.",

keywords = "Business news, Intercompany network, Revenue comparison, Social network analysis, Web mining",

author = "Zhongming Ma and Sheng, {Olivia R.L.} and Gautam Pant",

note = "Funding Information: Olivia R. Liu Sheng is Presidential Professor and Emma Eccles Jones Presidential Chair of Information Systems at the David Eccles School of Business, University of Utah. She also directs the Global Knowledge Management Center ( http://gkmc.utah.edu ) to seek research and education extension of data driven business optimization. Her research focuses on data mining and optimization techniques for ebusiness management, behavior targeting, personalization, recommendation, fraud/intrusion detection, bio-medical, digital government, telemedicine, telework and distributed learning applications. Her research has received funding from various Utah State agencies, Wasatch Advisors, Overstock, Optatio, U.S. Army, NSF, IBM, Tivoli, Toshiba Corp., Sun Microsystems, Hong Kong Research Grants Council, Asia Productivity Organization, SAP University Alliance, and Bureau of Land Management. She has published over 50 papers in such journals as Management Science, ACM Trans. on Information Systems, ACM Trans. on Internet Technology, Information Systems Research, INFORMS Journal on Computing, Communications of ACM, IEEE Trans. on Man, Machine and Cybernetics, IEEE Trans. on Biomedical Computing, and IEEE Trans. on Engineering Management. She is on the editorial board for various journals. ",

year = "2009",

month = nov,

doi = "10.1016/j.dss.2009.04.007",

language = "English (US)",

volume = "47",

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AB - Large volumes of online business news provide an opportunity to explore various aspects of companies. A news story pertaining to a company often cites other companies. Using such company citations we construct an intercompany network, employ social network analysis techniques to identify a set of attributes from the network structure, and feed the attributes to machine learning methods to predict the company revenue relation (CRR) that is based on two companies' relative quantitative financial data. Hence, we seek to understand the power of network structural attributes in predicting CRRs that are not described in the news or known at the time the news was published. The network attributes produce close to 80% precision, recall, and accuracy for all 87,340 company pairs in the network. This approach is scalable and can be extended to private and foreign companies for which financial data is unavailable or hard to procure.

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Discovering company revenue relations from news: A network approach

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Keywords

ASJC Scopus subject areas

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