Business health characterisation of listed Indian companies using data mining techniques

Senthil Arasu Balasubramanian, G.S. Radhakrishna, P. Sridevi and Thamaraiselvan Natarajan

International Journal of Business Information Systems, 2019, vol. 32, issue 3, 324-363

Abstract: The purpose of our study was to predict the business health of listed Indian companies using data mining tools and algorithms called ANN-MLP and DT-QUEST and identify financial ratios that significantly affect the company's performance. We used 2,000 listed Indian companies with 12,000 firm-year records (cases) from 2011 to 2016 to predict the financial performances of the companies and classify them as successful or unsuccessful, based on 17 financial ratios. The final sample of data was divided into training and test set (50:50, 60:40, 70:30 and 80:20). The test results confirmed accuracy between 84% and 86% for the MLP technique and between 92% and 93% for the QUEST technique. Sensitivity analysis results showed that return on long-term fund, net profit margin, and operating margin are three critical variables that affect business health.

Keywords: business health; multilayer perception; sensitivity analysis; decision tree; financial ratios. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=103079 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:32:y:2019:i:3:p:324-363

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().