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Business health characterization: A hybrid regression and support vector machine analysis
University of Borås, Faculty of Textiles, Engineering and Business. (Textile Management)ORCID iD: 0000-0003-2015-6275
TriloByte Statistical Software Ltd..
College of Engineering & Technology.
Technical University of Liberec.
2016 (English)In: Expert Systems with Applications, Vol. 49, p. 48-59Article in journal (Refereed) Accepted
Sustainable development
The content falls within the scope of Sustainable Development
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 49, p. 48-59
Keyword [en]
Business health, Credit rating, Predictive classification model, Support vector machine, Bankruptcy prediction, Variable selection
National Category
Economics
Research subject
Textiles and Fashion (General); Bussiness and IT
Identifiers
URN: urn:nbn:se:hb:diva-8306DOI: 10.1016/j.eswa.2015.11.027ISI: 000368959600004Scopus ID: 2-s2.0-84953455524OAI: oai:DiVA.org:hb-8306DiVA, id: diva2:890428
Available from: 2016-01-03 Created: 2016-01-03 Last updated: 2016-12-28Bibliographically approved

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Pal, Rudrajeet

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Citation style
  • apa
  • harvard1
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  • vancouver
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  • de-DE
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Output format
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