Business health characterization: A hybrid regression and support vector machine analysis

• 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.

Business health prediction is critical and challenging in today's volatile environment, thus demand going beyond classical business failure studies underpinned by rigidities, like paired sampling, a-priori predictors, rigid binary categorization, amongst others.In response, our paper proposes an investor-facing dynamic model for characterizing business health by using a mixed set of techniques, combining both classical and “expert system” methods. Data for constructing the model was obtained from 198 multinational manufacturing and service firms spread over 26 industrial sectors, through Wharton database.The novel 4-stage methodology developed combines a powerful stagewise regression for dynamic predictor selection, a linear regression for modelling expert ratings of firms’ stock value, an SVM model developed from unmatched sample of firms, and finally an SVM-probability model for continuous classification of business health. This hybrid methodology reports comparably higher classification and prediction accuracies (over 0.96 and ∼90%, respectively) and predictor extraction rate (∼96%). It can also objectively identify and constitute new unsought variables to explain and predict behaviour of business subjects.Among other results, such a volatile model build upon a stable methodology can influence business practitioners in a number of ways to monitor and improve financial health. Future research can concentrate on adding a time-variable to the financial model along with more sector-specificity.