Gupta, Sanjeev

Kumar, Kuldeep

Kumar, Nitin

Shrivastava, Arvind

English

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 purpose

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Corporate Distress Prediction Using Random Forest and Tree Net for India

https://pure.bond.edu.au/ws/files/36065273/2020_Corporate_distress_prediction_using_random_forest_and_tree_net_for_india.pdf

Corporate Distress Prediction Using Random Forest and Tree Net for India

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