Corporate Distress Prediction Using Random Forest and Tree Net for India

Arvind Shrivastava*, Nitin Kumar, Kuldeep Kumar, Sanjeev Gupta

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification andpredictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalJournal of Management and Science
Issue number1
Publication statusPublished - 2020


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