Abstract
This study aims to verify the potential of combining prior payment behavior variables and financial ratios for SE default prediction modelling. Logistic regression was applied to a sample of 980 Italian SEs in order to calculate and compare two categories of default prediction models, one exclusively based on financial ratios and the other based also on company payment behavior related variables. The main findings are: 1) using prior payment behavior variables significantly improves the effectiveness of SE default prediction modelling; ii) the longer the forecast horizon and/or the smaller the size of the firms which are the object of analysis, the higher the improvements in prediction accuracy that can be obtained by using also prior payment behavior variables as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.
Highlights
IntroductionCompany default prediction models and processes have been largely analysed in the literature (e.g., Aaron, Nainggolan, & Trinugroho, 2017; Altman, 1968, 1993, 2004; Altman, Brady, Resti, & Sironi, 2005; Altman & Sabato, 2005; Beaver, 1966; Blum, 1974; Figini, Savona, & Vezzoli, 2016; Grice & Ingram, 2001; Gupta, 2014; Huijuan, 2015; Ohlson, 1980; Pindado, Rodrigues, & De la Torre, 2008; Traczynski, 2017)
Many different traditional and non-traditional methodologies have been proposed in literature with the aim of developing company default prediction models and processes such as logistic regression analysis (Ohlson, 1980), multivariate discriminant analysis (Altman, 1968; Altman et al, 1977; Blum, 1974; Deakin, 1972; Edmister, 1972; Rosenberg & Gleit, 1994), back-propagation neural network (Tam, 1991), and artificial neural networks (Zhang et al, 1999; Ciampi & Gordini, 2013)
Both Model 1 and Model 2: 1) were initially developed at an aggregate level and separately designed for each of the four turnover size groups shown in Table 1; 2) were designed in order to predict default/non default at forecast horizons of one, two, and three years; 3) were assessed by testing their prediction accuracy on the holdout sample
Summary
Company default prediction models and processes have been largely analysed in the literature (e.g., Aaron, Nainggolan, & Trinugroho, 2017; Altman, 1968, 1993, 2004; Altman, Brady, Resti, & Sironi, 2005; Altman & Sabato, 2005; Beaver, 1966; Blum, 1974; Figini, Savona, & Vezzoli, 2016; Grice & Ingram, 2001; Gupta, 2014; Huijuan, 2015; Ohlson, 1980; Pindado, Rodrigues, & De la Torre, 2008; Traczynski, 2017). Most authors use only financial ratios calculated on the last published financial statements as default predictors. This approach limits the predictive capacities of the developed models because a company’s last published financial statements regard its past results while bankruptcy prediction should look at its future. Firms not rarely tend to postpone the accounting emergence of their financial and/or economic unbalances, thereby postponing the translation of a firm’s crisis into weak financial ratios. Official financial statements and financial ratios (which are based on these statements) tend to give a representation of a firm’s financial health that is out-of-date and of little use for effective default prediction processes
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