This paper applies mainstream statistical techniques (linear discriminant analysis and logistic regression) to a sample of over 6,000 Italian firms in the attempt to develop two distress prediction models, specifically constructed for SEs and taking into account diversity of size, geographical...

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

From as early as the 1960s, there have been a large number of studies aimed at assessing the application of statistical models to corporation data with a view to predicting business failure. This issue has become increasingly important in recent years, as the New Basel Capital Accord (Basel II)...

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...

A large number of empirical studies have used univariate and multivariate statistical methods when examining the effectiveness of appropriately selected corporation data in constructing company default prediction models. Having accurate evaluation methods has become increasingly important since...

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...