Predicting probability of default of Indian corporate bonds: logistic and Z‐score model approaches

Abstract

PurposeThis paper aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. At the same time, it also aims to present methods for directly estimating corporate probability of default (PD) using financial as well as non‐financial variables.Design/methodology/approachMultiple Discriminate Analysis (MAD) is used for developing Z‐score models for predicting corporate bond default in India. Logistic regression model is employed to directly estimate the probability of default.FindingsThe new Z‐score model developed in this paper depicted not only a high classification power on the estimated sample, but also exhibited a high predictive power in terms of its ability to detect bad firms in the holdout sample. The model clearly outperforms the other two contesting models comprising of Altman's original and emerging market set of ratios respectively in the Indian context. In the logit analysis, the empirical results reveal that inclusion of financial and non‐financial parameters would be useful in more accurately describing default risk.Originality/valueUsing the new Z‐score model of this paper, banks, as well as investors in emerging market like India can get early warning signals about the firm's solvency status and might reassess the magnitude of the default premium they require on low‐grade securities. The default probability estimate (PD) from the logistic analysis would help banks for estimation of credit risk capital (CRC) and setting corporate pricing on a risk adjusted return basis.

Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan

“…For example, Canada (Altman and Lavelle, 1981), Australia (Izan, 1984), UK (Charitou et al, 2004), France (Micha, 1984), Korea (Altman et al, 1995), Japan (Xu and Zhang, 2008), Malaysia (Bidin, 1988), Sudan (Eljelly et al, 2001), India (Bandyopadhyay, 2006), Turkey (Ugurlu and Aksoy, 2006), and Iran (Etemadi et al, 2008), among many others.…”

Predicting financial distress: Applicability of O-score model for Pakistani firms

“…liquidity, performance, and leverage. These two probabilistic models (logit and probit) are widely used in literature e.g Bandyopadhyay (2006), Bauer & Agarwal (2014), Duda & Schmidt (2010), and Shumway (2001), yet literature does not indicate the predictive power of these models affected by using the new data set over the original time and dataset used to develop these models. In addition, many other bankruptcy models are developed in literature including recursive partitioning (decision tree) analysis (RPA), neural network (NN), linear probability model (LPM), nevertheless Logit model remained the most used model for bankruptcy prediction after 2000 (Aziz & Dar, 2006).…”

Empirical Study on the Efficiency of the Companies Financing Process through Statistical Analysis

“…Beaver builds, in 1968, his own Z-score model using the multi-varied analysis. In the same direction and in the same year, Altman develops his classical model for predicting the risk of bankruptcy, applicable to companies in the publicity industry in the USA (Bandyopadhyay, 2006). If the first model (1968) offers a prevision period for the occurrence of the risk of bankruptcy of only two years, in Altman (1977) the author suggests an improved variant of the model, named ZETA, drawn for the American companies in the processing industry and in commerce, where the prediction interval increases to five years and in which, besides the discriminant analysis, non-linear models are used as well.…”

“…MacKieMason (1990) suggests a modified variant of Altman's model that can be used to calculate the delay until bankruptcy, developing, thus, an instrument for coordinating organizational activity and strategy. In the same sense, studies have been performed using modern techniques, such as the Logit and Probit models (Westgaard and Wijst, 2001;Grunert et al, 2005, Bandyopadhyay, 2006) applicable on various financial markets and in various economic locations.…”