Paper Info
Title
Using Rough Set Theory And Decision Trees To Diagnose Enterprise Distress - Consideration Of Corporate Governance Variables
Abstract
This study discusses the key factors of financial distress warning models for companies using corporate governance variables and financial ratios as the research variables, sieving out influential variables based on the attribute simplification process of rough set theory (RST). Then, we construct some classification models for diagnosing enterprise distress based on RST, using a data mining technique of decision trees with the selected indicators and variables. The empirical results obtained from analysis of enterprise distress indicators, show that financial distress is not only affected by the traditional financial ratios, but also by corporate governance variables. In addition, enterprise distress diagnosis models constructed based on RST and decision trees can effectively diagnose firms in times of crisis. In particular, the RST models are more accurate. This study provides a reference for better understanding the symptoms that might lead to a company's financial crisis in advance and thus provide a valuable reference for investment decision making by stakeholders.
Year
DOI
Venue
2014
10.1007/978-3-319-09339-0_20
INTELLIGENT COMPUTING METHODOLOGIES
Keywords
Field
DocType
data mining, enterprise distress diagnosis, financial ratios, corporate governance, rough set theory(RST), decision trees
Financial ratio,Distress,Decision tree,Corporate governance,Actuarial science,Financial crisis,Computer science,Rough set,Artificial intelligence,Management science,Financial distress,Machine learning
Conference
Volume
ISSN
Citations 
8589
0302-9743
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Fu Hsiang Chen1133.27
Der-Jang Chi2673.70
Chun-Yi Kuo300.34