Efficacy of industry factors for corporate default prediction

Studies on financial distress prediction have predominantly focussed on firm-specific factors, and the use of accounting information is more common. A limited number of studies consider the impact of industry factors on the risk of default. Even the few studies that do so use an industry dummy variable, which may lead to a biased assessment of the creditworthiness of all the firms belonging to the same industry. Moreover, it provides little information on how the firm’s sensitivity to the uncertainties in the relevant industry might affect its susceptibility to distress.

Departing from the existing literature, this paper is built on the conjecture that a firm need not necessarily face distress simply by virtue of belonging to a particular industry. This study is the first attempt to use a sensitivity variable for industry factors (industry beta) and to assess its impact on a firm’s default probability. The industry beta is estimated by regressing the monthly stock return of each individual firm on the monthly return of the respective sectoral or industry index. The study uses logistic regression and multiple discriminant analysis for matched pair sample of defaulting and non-defaulting listed Indian firms. The industry beta is found to be statistically significant in predicting defaults. Higher sensitivity to industry factors leads to an increased probability of default.  

The findings of the study have important implications for lending as well as investment decisions. The study highlights the significance of the sensitivity of a firm to uncertainties in the relevant industry and its impact on default risk. This establishes the fact that each firm is uniquely affected by the changes in the industry environment in which it operates. Hence, lenders and investors need to constantly monitor the sensitivity of a firm to these changes and understand its implications for default risk.