The value of financial ratio analysis in predicting the failure of JSE listed companies
Cassim, Ronel Juliana
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The objective of this study investigated the successful prediction of business failure of JSE listed companies using financial ratio analysis. During the research, financial statement data of failed and non-failed JSE listed companies during 2007-2012 financial periods were analysed, compared and interpreted. The interpretation of the trends and comparisons is of a quantitative nature, together with a qualitative genre which examines the tables, figures and equations in order to get the entire picture of the company’s performance for a five year period. The combination of literature on various failure predictor models and experience of these models resulted in the development of a modified model. The conclusion from the study indicated that financial ratio analysis successfully predicts failure and non-failure of the 16 companies that were investigated. These companies were grouped into eight delisted (failed) and eight listed (non-failed) JSE companies, which were paired in accordance to industry, fiscal period and closest asset size. The adoption of the traditional ratio analysis methods and EMS model yielded some interesting findings. The traditional ratio analysis methods (trend and comparative ratio analysis) were used with the Emerging Market Score (EMS) Model. The outcomes indicated the traditional methods are viable company failure prediction tools and the EMS model points out companies at a score of 2.60 and above as being financially stable. Between 2.60 and 1.10 the results are not very dependable because it is known that the company is in distress, yet uncertain whether the company has financially failed and below 1.10 the company has failed. It was concluded that a combination of the various prediction models enhances the accuracy of failure prediction. Therefore further research is required to assist stakeholders of South African companies to predict business failure by developing an adjusted model in a South African context.
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