The results of our statistical analyses, conducted on a sample of small manufacturing firms in Northern and Central Italy, show that both discriminant analysis and logistic regression are effective tools for designing SEs default prediction models based on economic-financial ratios

The results of our statistical analyses, conducted on a sample of small manufacturing firms in Northern and Central Italy, show that both discriminant analysis and logistic regression are effective tools for designing SEs' default prediction models based on economic-financial ratios

Previous empirical research shows the effectiveness of using sets of economic-financial ratios for company default prediction statistical modeling. However, such research rarely focuses on small enterprises (SEs) as specific units of analysis. In Italy, SEs account for more than 98% of all firms...

In our study, logistic regression was applied to a sample of 760 Italian small enterprises (SEs) in order to test the accuracy of a company default prediction model based on both economic-financial ratios and variables regarding managerial characteristics. We then compared the results obtained...

Purpose of the paper: To test whether the qualitative variables regarding the territory and/or the firm-territory relationship can improve the accuracy rates of small enterprise business failure prediction models.Methodology: We applied logistic regression to a sample of 736 Italian small...

Purpose of the Paper: To test whether the qualitative variables regarding the territory and/or the firm-territory relationship can improve the accuracy rates of small enterprise business failure prediction models.Methodology: We applied logistic regression to a sample of 736 Italian small...

From the 1960s onwards, a great number of studies focused on how to combine firm data with statistical classification models so as to predict business failure. This issue became more pressing in recent years after the Basel Capital Accords linked capital requirements to the banks' models for...