The Predictive Abilities of Financial Ratios in Predicting Company Failure in Malaysia Using a Classic Univariate Approach

This study examined sixty-four (64) listed companies over a period of ten years using a classic univariate method. Most studies on failure and bankruptcy predictions in the past forty years or more have been dominated by various multivariate statistical methods or some form of artificially intelligent systems. This study however, showed that the predictive powers of the individual ratios used individually and independently of each other has produced highly successful results. The means of the ratios showed significant differences between the companies that failed and those that were non-failed. A dichotomous classification test performed on the holdout sample using the cut-off point obtained from the analysis sample showed average classification accuracy of between 79% and 84%. One ratio, the Cash Flow to Total Debt perform particularly well with correct classification results of failed companies of between 81%% and 94% for both the analysis and the holdout sample and for all the four years before actual failure. Being able to classify failed companies with a high degree of accuracy is most significant as minimizing Type I errors are much more important to a wide range of users of financial information as the consequences of failure to predict failed companies are more devastating than failure to predict correctly the non-failed companies. For one individual ratio to achieve such high predictive abilities and for all four years before actual failure is surprising as this result is comparatively better than many studies using the more popular multivariate techniques where multiple ratios are analysed simultaneously. The study showed that financial ratios used individually do have strong predictive abilities though not all the ratios can predict equally well and for every year.