The objective of this study is to examine the performance of two default prediction models: the Z-score model using discriminant analysis, and the logit model on a dataset of 60 defaulted and 60 solvent companies. Financial ratios obtained from corporate balance sheets are used as independent variables while solvent/defaulted company (ratings assigned) is the dependent variable. Furthermore, for logistic regressions, an attempt is made to combine macro variables and dummy industry variables along with accounting ratios. The predictive ability of the proposed Z score model is higher when compared to both the Altman original Z-score model and the Altman model for emerging markets. The research findings establish the superiority of logit model over discriminant analysis and demonstrate the significance of accounting ratios in predicting default.
Gupta, V. (2014). An Empirical Analysis of Default Risk for Listed Companies in India: A Comparison of Two Prediction Models. International Journal of Business and Management, 9(9). https://doi.org/10.5539/ijbm.v9n9p223