Analyzing financial distress in the automobile industry: a comparative study of Altman Z-Score, Springate S-Score, Zmijewski Z-Score, and Grover G-Score

Abstract

Purpose: The main purpose of the present study is to examine the predictive quality of four financial distress models, including the Altman, Springate, Zmijewski, and Grover models, and the disparity in the capability of the models in predicting financial distress for the Indian automobile sector. Methodology: Logistic regression was utilized in the study, and the dependent variable is demonstrated as a binary variable. While the independent variables include the Altman Z-Score, Springate S-Score, Zmijewski Z-Score, and Grover G-Score, A subset of 10 automotive firms was selected. The secondary data was taken from the websites Money control, NSE, and BSE, and a 10-year time interval was taken from 2013–14 to 2022–23 for detailed evaluation over time. Result authentication and data configuration, along with their hypotheses, were determined by software like EViews 10 and Microsoft Excel 365. Findings: The results indicate that among the four independent variables, only the Zmijewski model shows a statistically significant relationship with the dependent variable, financial distress, and can predict distress with 62% accuracy. Practical Implications: The study’s insights are crucial, highlighting the significance of the Zmijewski model and guiding its prioritized use in distress analysis. The study also exposes the gaps among the distress models, enabling the ease of model selection for managers and investors. Originality/Value: This research will be extremely useful for financial distress model selection and financial risk management in the particular industry context.

Keywords: #Financial distress, #Altman’s Z-Score, #Springate S-Score, #Zmijewski Z-Score, #Grover’s G-Score, #Indian #Automobile Sector