Corporate default risk modeling under distressed economic and financial conditions in a developing economy

We create stepwise logistic regression models to predict the probability of default for private nonfinancial firms under distressed financial and economic conditions in a developing economy. Our main aim is to identify and interpret the drivers of private firm probability of default. For applicability and efficacy purposes, we apply a unique real-world data set of Zimbabwean private firms. Our experimental results show that the ratios of earnings before interest and tax (EBIT) to total assets, bank debt to total assets, EBIT to total liabilities, accounts receivable to net sales and the ratio of current assets minus current liabilities to total assets, the age of the firm, the real gross domestic product growth rate and the inflation rate are all strong drivers of probability of default for Zimbabwean private firms. We conclude that accounting information is useful in differentiating between defaulted and nondefaulted private firms under downturn conditions in a developing economy. Moreover, we determine that the forecasting results of probability of default models are improved by incorporating macroeconomic variables.