Abstract
The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs’ risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.
Highlights
The validity test on the small enterprises (SEs) discriminant function gives a global accuracy level of 80.58% with 18.74% of Type 1 errors and 20,10% Type 2 errors, while the same test on the SMLE discriminant function gives 20,80% Type 1 errors, 22,20%% Type 2 errors and an overall accuracy of 78.5%. These results confirm H1: small firms have their own specific structural and strategic characteristics, which are unlike those of large firms (Ciampi, 1994; Ciampi, 2015; Pompe & Bilderbeek, 2005) and their credit risk profiles are significantly different from larger companies
Financial institutions should use credit rating models built for SEs in order to maximize their capacity to create value for their shareholders (Altman & Sabato, 2007)
Building effective models for corporate default prediction is a strategic issue for banks when evaluating their present and potential clients, as well as for rating agencies
Summary
The effectiveness of financial ratios for company default prediction modelling has long been analyzed and assessed in literature (e.g., Altman, 1968; Aaron, Nainggolan, & Trinugroho, 2017; Beaver, 1966; Blum, 1974; Figini, Savona, & Vezzoli, 2016; Grice, & Ingram, 2001; Gupta, 2014; Huijuan, 2015; Ohlson, 1980; Pindado & Rodrigues, 2008; Traczynski, 2017).SEs constitute a relevant part of every national economic system (Berger & Scott 2007). SEs are normally quicker and more flexible when reacting to environmental (competitive, technological, social, etc.) changes and have a notably “personal” character, with owners and managers who are often one and the same people (Burke & Jarrat 2004; Ciampi, 2015). From this it follows that building and testing instruments developed in order to capture, interpret and asses these particular characteristics represent promising research exercises (Ciampi & Gordini, 2013)
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