Corporate bankruptcy prediction: re-estimating classical accounting-based models with a sample from Portugal

Purpose We aimed to test the predictive accuracy of three widely used accounting-based corporate bankruptcy prediction models (Altman’s Z”-score, Ohlson’s model and Zmijewski’s model) and re-estimate the models’ coefficients to improve accuracy and help small and medium-sized companies (SMEs) better assess their corporate clients’ credit risk in a European context. Design/methodology/approach We estimated corporate bankruptcy risk for a comprehensive sample of 9,371 companies from Portugal (of which 370 were bankrupt) using Altman’s Z”-score as well as Ohlson’s and Zmijewski’s models. We compared the models’ predictions to each company’s actual status (bankrupt or non-bankrupt). We re-estimated the coefficients of the three models using a logit regression methodology to improve the accuracy of predicting bankrupt companies. We tested the new equation coefficients both in-sample and out-of-sample and conducted a robustness test on 228 companies from nine other European countries. Findings The original models still demonstrate an overall accuracy (percentage of correctly predicted bankrupt and non-bankrupt companies) above 80%. However, they produce high errors in predicting bankruptcy only (Type I errors of 27.03% in Altman’s model, 22.97% in Ohlson’s model and 16.76% in Zmijewski’s model). While maintaining overall accuracy above 80%, the re-estimated equations reduced these errors to 13.24% in Altman’s model and 15.68% in Ohlson’s model. However, it failed to reduce Zmijewski’s model errors, which increased to 18.38%. The reduction in errors compared to the original models was also evident in out-of-sample testing. We conclude that Ohlson’s re-estimated model provides more balanced results regarding Type I errors (15.68%) and overall accuracy (84.35%). Originality/value This article contributes to the literature on corporate bankruptcy prediction by re-estimating the coefficients of three widely used accounting-based models with data from Portugal, showing that the new equations perform well within their market context. We believe that it will help SMEs measure the bankruptcy risk of their corporate clients with readily available accounting information.