3. Electronic Theses and Dissertations (ETDs) - All submissions
Permanent URI for this communityhttps://wiredspace.wits.ac.za/handle/10539/45
Browse
2 results
Search Results
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.Item Understanding the relationship between business failure and macroeconomic business cycles: a focus on South African businesses(2017) De Jager, MarinusThis study examined the relationship between business failure and macroeconomic fluctuations within business cycles of South Africa’s economy for the time period 1980 to 2016. The study also sought to understand where, if any, immediate and lag correlations between fluctuations and business failure could be established. To understand this connection, this study used longitudinal data sets of different macroeconomic factors and studied their influence on business failure. The vector error correction model (VECM) was used to determine the long-term relationship between failure and each of the other variables. Additionally, Granger Causality was applied to establish whether the macroeconomic variables investigated in this study can be constructed to predict the probability of business failures. Three classes of macroeconomic predictor variables were considered. Firstly, well-known international variables in the form of GDP and CPI were used. Secondly, the study incorporated the three Composite Business Cycle indicators- leading, coincident and lagging. Lastly, behavioural indicators were used to incorporate the views of the actual businesses and their customers, which for this the study were the Business and Consumer Confidence Indices. After examining the effects the 7 macroeconomic variables had on business failure, the study found that there is a long-run relationship between the Composite Lagging Business Cycle indicator, the Business Confidence and Consumer confidence, which influenced Business Failure. Additionally, it was noted that Business Failure influence the Composite Lagging Business Cycle indicator in the long-run. The study additionally found that Business Failure may Granger Cause the Composite Leading Business Cycle indicator Outcomes of the study are potentially vital for entrepreneurs to understand the timing of entry into markets based on macroeconomic fluctuations through their cycles in certain industries. Business owners can make proactive financial and strategic decisions vital for survival of their business through the expansion and especially in the contraction cycles of the macroeconomic environments.