On the non‐financial information's significance in the business failure models

Purpose – The purpose of this paper is to focus on the development of a business failure prediction model on a sample of small and medium‐sized firms with head offices located in the region of Castilla y León (Spain), in order to prove the significance of non‐financial information on the prediction of business failure. Design/methodology/approach – In order to reach the authors' aim, one of the most used predictive statistical methods in this field (logistic regression) is applied, in which the authors consider financial ratios and non‐financial information as potential variables to predict failure. But before developing the respective models, in order to reduce the number of variables, a principal components analysis (PCA) is first applied. Then, the achieved results with this analysis are used in the prediction step, so as to estimate the models. Findings – The results of the predictive method show that non‐financial information, which becomes significant in the developed models, helps financial ratios to improve the ability to predict failure, so any business failure model should also consider both types of information to be accurate. Originality/value – Most of the developed business failure prediction models have used a paired sample with the same number of failed and non‐failed firms, which has the drawback of not being representative of the population from which it is chosen. In order to obtain a representative sample, a random sampling method is applied, on the basis of the population size and composition. The selected sample assures that parameter estimates are not inconsistent and biased, as the statistical methods assume.