Predicting financial distress of JSE-Listed companies using Bayesian networks - Cassim, Ziyad (Division of Actuarial Science, 2016)
This study aims to test the suitability of using Bayesian probabilistic models to predict bankruptcy of JSE-listed companies. A sample of 132 companies is considered with fourteen years of financial statement information and macroeconomic indicators used as predictor variables. Various permutations of Bayesian models are tested relating to different learning algorithms, intervals of discretisation and scoring metrics. In contrast to previous research, we explore a variety of evaluation measures and it is found that predictive accuracy for bankrupt firms does not exceed 70% in any model augmentation. On comparison to other popular models such as the Altman Z-score and the logit model, it is found that Bayesian networks produce marginally better predictive accuracy. Furthermore, a comparison to previous research on the same subject is carried and reasons for significantly different results are considered. Finally, the reasons for low predictive accuracies is considered with issues relating specifically to South Africa being discussed.