A corporate failure prediction model for non financial South African corporates incorporating best practices used by the credit industry
In the context of the current macroeconomic environment there is an expectation of an increase in South African non financial corporate failure, where advance prediction thereof will become even more important. A number of South African non financial corporate failures have occurred following the financial crisis. In addition, South Africa experienced a watershed moment with the first default on a non financial corporate bond in 2013. At the same time, with the adoption of the International Financial Reporting Standards (IFRS) framework there have been significant advances in the quality of financial information which should improve its usage in predicting corporate failure. This study used the latest sample to date of listed South African non financial corporates that met the definition of failure but limited the universe of financial information to that which was prepared under IFRS. At the same time, adjustments were made to the financial data based upon pre selection of independent credit statistic variables most commonly used in ranking relative credit risk for nonfinancial corporates. Additionally, equity market price data was introduced into the model to add a forward looking information consideration. This resulted in an eleven variable model where differentiation of corporate failure was facilitated through the use of multiple discriminant analysis. The initial model resulted in an 82.95% classification rate (overall accuracy rate for correctly classifying failed and non failed firms) but this was further improved by using a three variable model comprising Revenue measured in US dollars, Debt/ Market Capitalisation and Debt/EBITDA. The inclusion of Debt/Market Capitalisation assured the consideration of forward looking information but also factored distance to default theory employed by market based models. This model, although with a slightly lower classification rate of 80.68% significantly improved the bias of the model to having a greater tendency to correctly classify failed firms. This was further validated through splitting the original sample in two with in sample and out of sample testing undertaken on each sample. The inclusion of outliers in financial data was shown to improve the ability to predict corporate failures. The study also concluded that samples using only financial data prepared under IFRS resulted in higher classification rates and therefore samples should only rely upon such financial data. Foreword A word of gratitude first and foremost to Professor Carlos Correia, who has believed in me and had faith in my ability right from the beginning when I entered the Masters in Financial Management program. This submission would not have been possible without his continued and unwavering support. Secondly, to the program itself, which sparked my interest in non financial corporate failure prediction and lead me to follow the career path that I have been on covering non financial corporate credit for over five years now at Moody's Investors Service. Also to Associate Professor Dr Glen Holman for always inspiring me to go Citius Altius Fortius in my career endeavours. Then to Associate Professor Dr Francois Toerien and Associate Professor Dr Ryan Kruger, who made my Masters possible and also encouraged my academic pursuits through the platform that they provided while I was a lecturer in the School of Management Studies' Finance Section. Not forgetting, Emeritus Professor Tim Dunne, Dr Kutlwano Ramaboa and Dr Ian Durbach, who brought about my love for statistical sciences and for Kutlwano for encouraging me to share my knowledge with students as a teaching assistant at the Graduate School of Business. Most importantly, to Ian for growing my knowledge in multivariate statistics and giving me the opportunity of a tutoring position on the third year research and survey statistics course, which only reinforced my understanding and grew my knowledge. This undertaking would have not been possible without the comprehensive knowledge that I gained in the application of discriminant analysis. And leaving the best for last, to my darling fiancee, Dr Jessica Groenewald, who has been by my side through thick and thin, across continents, many late nights and holidays worked and for most importantly never giving up on me and my completing this half dissertation submission. Table of contents Chapter