3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Predicting financial distress in South Africa: the role of macroeconomic factors(2018) Ntuli, Mduduzi Percy MphoRecent South African studies into corporate financial distress prediction have focused on multivariate models in which only financial ratios, computed from annual financial statements, are used as predictors. These South African financial ratio models, developed using multiple discriminant analysis, are limited to a corporate’s internal financial condition without consideration of the external factors that could result in financial distress. The increase in South African corporate failures during or following economic crisis has made it clear that external factors are important predictors to be applied in conjunction with internal financial ratios. International research has shown that financial distress depends on a corporate’s financial position, characteristics, and macroeconomic conditions. Different techniques have been applied by researchers to develop prediction models with these factors as predictors. Altman et al. (2017) used international data, excluding South Africa, to develop a logistic regression model based on financial ratios, firm characteristics, industrial sector and country risk. An empirical study is required to determine the applicability of this new model to predicting South African corporate failures. Furthermore, it is pertinent to determine if using South African corporate failure data improves the accuracy of the international model. In this report, the focus is on 99 Johannesburg Stock Exchange listed firms from 2000 to 2015 of which 34 failed and 65 are non-failed. For each firm in the sample, cross-sectional data consisting of financial ratios, firm characteristics, industrial sector and macroeconomic variables is collected. The financial ratios considered are: working capital to total assets; retained earnings to total assets; earnings before interest and tax to total assets; and book value of equity to total debt. The size of the firm and its age are taken as non-financial characteristics. Industrial sectors considered are: construction & materials; industrial goods & services; and technology industrial sectors. Annual economic growth, annual inflation, and average annual lending rates are taken as indicators of the macroeconomic environment. Six hypotheses are postulated to investigate the accuracy of a logistic regression or logit model in predicting financial failure within one year. Seven different models examine the classification performance when four financial ratios are combined with firm size, firm age, macroeconomic variables, and different industrial sectors. The classification accuracy of the models is measured by the Area Under the ROC Curve (AUC). Empirical results indicate that all models, using South African firm data, perform better than the international model and have predictive accuracies above 90% for in-sample predictions within one year. The highest predictive accuracy is achieved by a model containing all variables investigated in this study. For out-of-sample predictions, this model correctly classified 91% of non-failed and 100% of failed firms one year in advance. This study provides evidence that classification accuracy of iii financial ratio models can be improved by considering firm characteristics, the industry sector in which the firm operates, and the macroeconomic environment.