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.
Highlights
Entrepreneurship and robust corporate institutions are vital assets of modern states that create value and employment opportunities [1, 2]
Bapat and Nagale [9] performed an assessment of forecasting prowess of various statistical techniques for listed firms in Indian exchanges like logistic, discriminant, and neural network
Advanced statistical strategies like decision trees, Random Forest (RF), and stochastic gradient boosting has been applied by Halteh et al [10] to improve the understanding about credit-riskphenomenon
Summary
Entrepreneurship and robust corporate institutions are vital assets of modern states that create value and employment opportunities [1, 2]. Extensive usage of logistic modeling has been applied for categorizing and predicting failure of firms [4,5,6,7] (Shrivastava, 2018; Hua et al, 2007; Altman et al, 1994; Ohlson, 1980). Bapat and Nagale [9] performed an assessment of forecasting prowess of various statistical techniques for listed firms in Indian exchanges like logistic, discriminant, and neural network. The RF methods perform well in many real problems as compared to boosting and is convenient to train and tune as per situation. This has resulted in wide popularity of RF technique, implemented in variety of scenarios.
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