Is Altman's Model efficient in predicting bankruptcy? – A comparison among the Altman Z-score, DEA, and ANN models. Issue 6 (18th August 2022)

• Record Type: Journal Article
• Authors: Ghosh, Aniruddha; Kapil, Sheeba
• Is Part Of: Journal of information & optimization sciences. Volume 43:Issue 6(2022)
• Page Start: 1191
• Page End: 1207
• Publication Date: 2022-08-18
• Subjects: G33 (Bankruptcy) -- C33 ( Panel Data Model) -- C45 (Neural Network) -- C61 (Optimization) -- Dynamic Analysis; Bankruptcy -- Sustainability -- Altman Model -- DEA -- ANN; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2022.2117322
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: This study represents how firms can be prevented to become bankrupt by adopting the best bankruptcy prediction model and thus taking corrective action in order to avoid the firm from becoming unsustainable. Empirical evidence justifies that the involvement of various variables and new modeling techniques like ensemble and machine learning models helps in the improvement of prediction accuracy. However, most of the machine learning algorithms are paralyzed with the disability to capture time invariance issues in the model. To eliminate such delinquencies, we suggest Data Envelopment Analysis (DEA) model and neural network (NN) model to replace the existing Altman model for benchmarking purposes. The study is implemented on Indian steel companies classified on the basis of the national industries classification (NIC) codes because of the less interest coverage ratio and the highest percentage of default loans. The period of study was taken between 2015 to 2018, as there were several changes in the legal and fiscal structure in India. On running the experiment, it was observed that the cost of misclassification was high in the case of the Z-score model as compared to the DEA model and the ANN model. The study is an eyeopener for academia to implement non-linear models like DEA and NN for bankruptcy prediction research. The industry would also prefer to adopt more reliable models which shows lower misclassification cost. This paper also works as an anecdote for government and policymakers to prevent the firm from going into bankruptcy if adopted on projected figures of the company financials and hence, could prevent economic disruptions in terms of loss of creditors in terms of payment and the society in terms of employment.