Share repurchases, rather than dividend payments, are increasingly becoming the globally favoured payout method. This has prompted a renewed interest in the field, and raises questions about the actual motivation for share repurchases and whether companies are now repurchasing shares in preference to investing in future growth. This study set out to ascertain whether South African company payout behaviour mirrors global company behaviour. Comprehensive data on share repurchases are, however, not compiled by South African financial data sources or by the Johannesburg Stock Exchange Ltd. In preparation for this study, the authors thus compiled the first comprehensive share repurchase database for companies in selected JSE-listed sectors for the first 11 years (i.e. 1999 to 2009) since share repurchases were first allowed in this country.Share repurchases were found to be a popular payout method, especially in the more recent periods covered in the study. Payout value was dominated by a few companies paying dividends every year and regularly repurchasing shares. Aspects unique to the South African regulatory environment, however, resulted in the South African share repurchase experience not fully mirroring current global practice. The main constraint in the South African share repurchase environment is that comprehensive, actual-time-based share repurchase data are not available. Recommendations are made on how to align the South African regulatory environment with global best practice. Regulatory changes, as well as continued research in the field, will equip stakeholders to make informed decisions.

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In 2006, Steyn-Bruwer and Hamman highlighted several deficiencies in previous research which investigated the prediction of corporate failure (or financial distress) of companies. In their research, Steyn-Bruwer and Hamman made use of the population of companies for the period under review and not only a sample of bankrupt versus successful companies. Here the sample of bankrupt versus successful companies is considered as two extremes on the continuum of financial condition, while the population is considered as the entire continuum of financial condition.The main objective of this research, which was based on the above-mentioned authors’ work, was to test whether some modelling techniques would in fact provide better prediction accuracies than other modelling techniques. The different modelling techniques considered were: Multiple discriminant analysis (MDA), Recursive partitioning (RP), Logit analysis (LA) and Neural networks (NN).From the literature survey it was evident that existing literature did not readily consider the number of Type I and Type II errors made. As such, this study introduces a novel concept (not seen in other research) called the “Normalised Cost of Failure” (NCF) which takes cognisance of the fact that a Type I error typically costs 20 to 38 times that of a Type II error.In terms of the main research objective, the results show that different analysis techniques definitely produce different predictive accuracies. Here, the MDA and RP techniques correctly predict the most “failed” companies, and consequently have the lowest NCF; while the LA and NN techniques provide the best overall predictive accuracy.

In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to first test for stationarity, co-integration and causality. In testing for causality, the variables should be stationary. If non-stationary, one can estimate the model in difference form, unless the variables are co-integrated. This article determines whether cash flow and earnings variables are stationary, and which variable causes the other, using econometric analysis. In most cases, cash flow variables are found to cause earnings variables. This is so when the models are estimated in levels. However, when estimated in first differences, the causal relationship tends to be reversed such that earnings cause cash flows. Further study is recommended, whereby panel data could be used to improve the power of the tests.

The deficiencies in previous research on failure prediction studies were identified from the international literature. This study’s purpose is to address these deficiencies while using a new method in developing failure prediction models, namely recursive partitioning (specifically the classification tree algorithm).The deficiencies were addressed as follows:Brute empirism was avoided by focussing on cash flow ratios in combination with certain accrual ratios. Failure was not only defined as bankruptcy, but as any condition where the company cannot exist in future in its current form, therefore including delistings as well as major structural changes. By using the population of listed industrial companies between June 1997 and May 2002, the grey area in-between ‘successful’ and ‘bankrupt’ was included in developing the models. Every model developed was tested with the help of an independent sample. The different economic cycles were considered by developing different models for a growth and a recessionary period. A combined model was also developed, with the economic cycle as a independent dichotomous variable.When the prediction accuracy for the different classes and in total, of the models developed, is compared with the ex ante probability that an observation will fall in a particular class of the majority (non-failed companies), the prediction accuracy is in every instance higher than the ex ante probability.