Discovering company revenue relations from news: A network approach

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 at...

Full description

Saved in:
Bibliographic Details
Published in:Decision Support Systems 2009-11, Vol.47 (4), p.408-414
Main Authors: Ma, Zhongming, Sheng, Olivia R.L., Pant, Gautam
Format: Article
Language:English
Subjects:
Applied sciences
Business
Business news
Company structure
Computer science
control theory
systems
Construction equipment
Data processing. List processing. Character string processing
Exact sciences and technology
Intercompany network
Memory organisation. Data processing
Network analysis
Networks
News
On-line systems
Operational research and scientific management
Operational research. Management science
Portfolio theory
Revenue
Revenue comparison
Revenues
Scalability
Social network analysis
Social networks
Software
Studies
Web mining
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2009.04.007