Corporate failure diagnosis in SMEs: A longitudinal analysis based on alternative prediction models

Purpose –The main purposes of this paper are to provide evidence about corporate failure diagnosis in SMEs, identify the predictor variables that enhance the accuracy of the corporate failure diagnosis models, and perform comparative analysis of the proposed models with the existing literature. The paper supports the proposition that the majority of the proposed corporate failure diagnosis models in the literature exhibit an endogenous drawback since their construction is based on large entities or listed corporations' samples. Design/methodology/approach –The present study employs multiple discriminant analysis, logit analysis, and probit analysis to construct corporate failure diagnosis models based on SMEs longitudinal data from Greece. Findings – The paper provides evidence that the contribution of human capital is immensely more important to the viability of SMEs than to the viability of large corporations. Moreover, this study identifies interactions among seemingly insignificant variables that exhibit incremental information content and attribute massive discriminant power to the proposed corporate failure diagnosis models. Practical implications -The results of this study encourage regulatory authorities to adopt enhancements to the Basel II framework and financial institutions as regards to constructing their corporate failure diagnosis models. The models is based upon internal default experience and mapping to external data incorporating both quantitative and qualitative variables. Originality/value – The contribution of this paper is the proposition of new value-relevant variables that enhance the accuracy of existing corporate failure diagnosis models for SMEs.