A Cash Flow Based Model of Corporate Bankruptcy in Australia

Data AvailabilityAll data available from public and commercial sources identified in the paperIntroductionThe prediction of corporate distress has attracted the attention of accountants, regulators, business analysts and financial economists over the last five decades (Altman 2002). As pointed out by Jones and Hensher (2008) bankruptcy modelling has enjoyed a major resurgence of interest since the global financial crisis. Jones and Hensher (2004) observe that bankruptcy forecasts are now widely used for a range of purposes, including evaluating the solvency of financial institutions and corporations by resource providers and regulators, assessment of debt security by lending organisations, auditor evaluations of going concern, and the pricing of bonds, credit derivatives and other instruments exposed to credit risk.While more sophisticated modelling techniques have emerged in the literature, much of the extant literature has relied on multiple discriminant analysis (MDA), binary logistic or probit analysis and in a small number of cases multinomial logit models (MNL) (see e.g., Altman 1968; Altman, Haldeman, and Narayanan, 1977; Ohlson, 1980; Zmijewski, 1984; Altman, 2002; Jones and Hensher, 2008). The early work of Altman (1968) championed the use of MDA; however, logit models have become more prevalent in the literature at least from the 1980s, starting with the work of Ohlson (1980). Based on an analysis of 150 empirical studies, Jones et al., (2015) observes that the logit model is the most commonly used statistical model in the corporate bankruptcy literature over the past 50 years.There are several possible reasons for this. First, logit models are based on less rigid statistical assumptions than MDA. This can be important as bankruptcy datasets tend to "noisy", marred by missing values, incomplete data and input variables which rarely conform to the normality condition (due to the effects of outliers, skewness, kurtosis and other issues in the dataset). Logit models tend to perform better when the dataset is not 'well behaved'. As MDA is based on more rigid econometric assumptions (such as multivariate normality and IID) parameter estimates can easily be biased and predictive performance undermined when these conditions are not fulfilled.Greene (2008) elegantly describes the differences between MDA and discrete choice models such as logit and probit (in this case he uses the example of loan applicants):"It's long track record notwithstanding, one could argue that the underpinning of discriminant analysis is naive. The technique divides the universe of loan applicants into two types, those who will default and those who will not. The crux of the analysis is that at the time of application, the individual is as if preordained to be a defaulter or a nondefaulter. In point of fact, the same individual might be in either group at any time, depending on a host of attendant circumstances and random elements in their own behaviour. Thus, prediction of default is not a problem of classification the same way as is, say, determining the sex of prehistoric individuals from a fossilized record.Index function based models of discrete choice, such as the probit and logit models, assume that for any individual, given a set of attributes, there is a definable probability that they will actually default on a loan. This interpretation places all individuals in a single population. The observed outcome, default/no default, arises from the characteristics and random behaviour of the individuals. "Hence, a second reason for the dominance of logit models in the literature comes down to their intuitive appeal, convenience and practicality. Logit models are probability models and the outputs of these models represent probability estimates of corporate failure. Probability outcomes are relatively easy for practitioners to interpret. However, MDA models produce a Z score, which is a cut-off threshold. …