Business Failure Prediction: Simple-Intuitive Models versus Statistical Models

Abstract

This paper gives an overview of the shortcomings of the most frequently used statistical techniques in failure prediction modeling. The statistical procedures that underpin the selection of variables and the determination of coefficients often lead to ‘overfitting’. It is also seen that the ‘expected signs’ of variables are sometimes neglected and that an underlying theoretical framework mostly does not exist. Based on the current knowledge of failing firms, this paper constructs a new type of failure prediction model, namely ‘Simple-Intuitive Models’ (SIM). In these models, eight variables are first logit-transformed and then equally weighted. These models are tested on two broad validation samples [one year prior to failure (ypf) and three ypf] of Belgian companies. The performance results of the best simple-intuitive model is comparable to those of less transparent and more complex statistical models.