AN EXAMINATION OF THE STATIONARITY OF MULTIVARIATE BANKRUPTCY PREDICTION MODELS - A METHODOLOGICAL STUDY

Previous bankruptcy prediction studies using multiple discriminant analysis (MDA) of financial ratios exhibit a lack of consistency both in the values of the coefficients reported and the relative importance of the various financial ratios when used in different studies. Part of this inconsistency can be attributed to the use of different sets of ratios, since no two studies used exactly the same set. Since multiple discriminant analysis coefficients are unique only up to a factor of proportionality (Eisenbeis [1977] and Ladd [1966]), the use of a different functional form of the MDA model of different subsets of a given set of financial ratios can result in different coefficient values. Apart from the selection of different ratios in the final prediction models, other methodological issues are raised by some common practices followed in bankruptcy prediction studies. For example, researchers typically pool data across different years without considering the underlying economic events in those years. Futhermore, in the development of the final prediction models, some authors consciously attempt to control for multicollinearity, whereas others ignore the issue, relying on Eisenbeis' assumption that multicollinearity is not a problem in discriminant analysis if classification accuracy is the objective (Eisenbeis [1977]). The