Why do companies fail? Considering the key problems and success factors in modelling failure prediction in an Australian context
Corporate failure is a regularly recurring problem for stakeholders, particularly investors, creditors and customers. Early attempts at predicting such failure typically relied on analysis of individual performance measurements such as accounting ratios; it was not until the late 1960s that a modelling approach to the problem started to evolve. Altman's Z-score model was the first approach to combine a series of weighted ratios using the statistical technique of multiple discriminant analysis (MDA) to arrive at a final score, which was used to determine whether or not a company was likely to fail. Substantial research has followed over the subsequent 40 years, resulting in model variants ranging from slight changes to the seminal Z-score approach, and finally to totally different approaches using a range of statistical tools. This paper looks at the question of whether modelling can be an effective tool for failure prediction. It firstly looks at previous research in this area, particularly with regard to the shortcomings of modelling approaches in general, but also at some key problem areas for models such as defining what constitutes a failure event, some of the perceived specific shortcomings of previous failure modelling attempts and issues around the impact of creative accounting. The impact of non-financial factors is considered, leading to some conclusions on the practicality of incorporating such information in a model environment. The new multi-dimensional modelling approach introduced is based on Australian source data from three related industry groupings and focuses on providing a failure probability result rather than a defined fail/non-fail conclusion. The paper concludes by discussing some of the key contributing factors identified in the development of the model, including the clear evidence of predictive value incorporated in cash flow information, particularly cash flows from operations.