dc.contributor.advisor |
Brummer, L.M., 1940- |
en |
dc.contributor.coadvisor |
Wolmarans, H.P. (Hendrik Petrus) |
en |
dc.contributor.postgraduate |
Sabela, Sibusiso Wellington |
en |
dc.date.accessioned |
2017-05-18T08:34:57Z |
|
dc.date.available |
2017-05-18T08:34:57Z |
|
dc.date.created |
2017-05-04 |
en |
dc.date.issued |
2016 |
en |
dc.description |
Thesis (PhD)--University of Pretoria, 2016. |
en |
dc.description.abstract |
This study presents a three-tiered approach to determine financial distress in companies listed on the Johannesburg Stock Exchange. The objective of this unique approach is to contribute to the existing knowledge base in the study of financial distress prediction. The three-tiered approach sees the development of a: (i) basic model, (ii) Merton model, and (iii) hybrid model. The basic model is further split in three phases. In the first phase the model is based on fundamental data; the second phase adds market variables; and the third phase adds macroeconomic indicators. The first phase points to various company specific ratios, the second phase points to various market based ratios and the third phase points to external economic indicators. Pioneered by Merton (1974:449), the Merton model is a structural model with its framework adopted from the Black-Scholes option pricing methodology. Therefore, the hybrid model is a combination of the basic and Merton models.
This study explores the effectiveness of a hybrid model, in which both the fundamental and market data are used as input variables. This combination is intended to enhance the predictive power of a company's default event, given that both variables convey company-specific credit risk information that is not considered by the other.
In developing the basic model, this study focuses on exploring a multinomial approach where companies are categorised in three groups: distressed, depressed and healthy. This is in line with the thinking that failure does not affect companies immediately, but is rather a process. Healthy companies go through a depression phase before they actually fail.
The statistical technique of choice for the basic and hybrid models is the multinomial logistic regression. This technique is chosen on its strength over alternatives like multi-discriminant analysis, with the nature of data being the driving force. Certain statistical tests were performed on the data, like the Kolmogorov-Smirnov and Shapiro-Wilk statistical tests of data normality. The sample of companies used in the present study is categorised as follows; 8% distressed, 14% depressed, and 78% healthy. Given that the percentage number of companies in each category is not equal, the statistical integrity of multi-discriminant analysis would be grossly compromised.
The Merton model is based on the formula as derived by its pioneer. This mathematical formula uses five estimated variables: asset value, asset volatility, debt level, risk-free rate, and time. The fundamental assumption of structural models is that there is a cause-effect, economically motivated reason why firms default. Default is highly likely to occur when the market value of a firm's assets is insufficient to cover its liabilities in the future. This balance sheet approach to measuring risk means that the market-based models share common ground with fundamental models in credit analysis. However, a major advantage of market-based models over the fundamental approach is that they provide both timely warning of changes in credit risk and an up-to-date view of a firm's value. This view is given on the basis that market prices are indicative of future cash flows of the business.
The most important motivation to study both these models and further develop a hybrid model within the South African market is the lack of such academic research in the local academic domain. Therefore, this uniquely positions the study where the distress probability is studied by applying both fundamental and market data. This study also aims to investigate which of the two models is better at differentiating defaulting and non-defaulting firms. In this way, the study assesses the extent to which different failure prediction models may yield significantly different rankings for the same firm. Furthermore, the study explores the extent of gains (if any) that can be realised by combining the two models' predictions.
The present study is based on information sourced from the Johannesburg Stock Exchange, INET BFA, South African Reserve Bank and other relevant academic material. To be included in the sample, firms are required to have a minimum listing period of at least 24 months to ensure that the firm's market price reflects the market's collective opinion of the prospect of its business. For purposes of the fundamental data, companies are required to have existed for at least five years to be included in sample. The economic period under review in this study is 2005-2014. The 2014 cut-off is set to ensure the availability of financial statements. The study has a sample size of 100 companies, consisting of eight distressed, 14 classified as depressed, and 78 healthy. |
en_ZA |
dc.description.availability |
Unrestricted |
en |
dc.description.degree |
PhD |
en |
dc.description.department |
Financial Management |
en |
dc.identifier.citation |
Sabela, SW 2016, A three-tier approach to determine financial distress of companies listed on the Johannesburg Stock Exchange, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/60519> |
en |
dc.identifier.other |
A2017 |
en |
dc.identifier.uri |
http://hdl.handle.net/2263/60519 |
|
dc.language.iso |
en |
en |
dc.publisher |
University of Pretoria |
en |
dc.rights |
© 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
en |
dc.subject |
UCTD |
en |
dc.title |
A three-tier approach to determine financial distress of companies listed on the Johannesburg Stock Exchange |
en_ZA |
dc.type |
Thesis |
en |