Vol. 1 No. 1 (2020): Vol 1, Iss 1, Year 2020
Highlighted Article

Corporate distress prediction using random forest and tree net for india

Arvind Shrivastava
The views expressed in this paper are strictly personal and not related to authors’ respective institutional affiliation.
Nitin Kumar
The views expressed in this paper are strictly personal and not related to authors’ respective institutional affiliation
Kuldeep Kumar
The views expressed in this paper are strictly personal and not related to authors’ respective institutional affiliation.
Sanjeev Gupta
The views expressed in this paper are strictly personal and not related to authors’ respective institutional affiliation
Published February 20, 2020
Keywords
  • financial distress, insolvency risk, prediction, random forest, tree net
How to Cite
Shrivastava, A., Kumar, N., Kumar, K., & Gupta, S. (2020). Corporate distress prediction using random forest and tree net for india. Journal of Management and Science, 1(1), 1-11. https://doi.org/10.26524/jms.2020.1

Abstract

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 and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.

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