Business health characterization: A hybrid regression and support vector machine analysis
作者：
Highlights：
• Business health characterized using stagewise regression and support vector machine.
• Analyzed about 200 most significant US firms with both low and high rating.
• Regression model extracted financial ratios at 96.6% rate to model expert rating.
• Support vector machine classified firms good or bad with 89% prediction accuracy.
• Devised quantitative probabilistic predictive classification for rating of firms.
摘要
•Business health characterized using stagewise regression and support vector machine.•Analyzed about 200 most significant US firms with both low and high rating.•Regression model extracted financial ratios at 96.6% rate to model expert rating.•Support vector machine classified firms good or bad with 89% prediction accuracy.•Devised quantitative probabilistic predictive classification for rating of firms.
论文关键词：Business health,Credit rating,Predictive classification model,Support vector machine,Bankruptcy prediction,Variable selection
论文评审过程：Received 11 April 2015, Revised 25 November 2015, Accepted 26 November 2015, Available online 15 December 2015, Version of Record 5 January 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.11.027
