Abstract
<p>This paper investigates the performance of forecasting models for default risk referring to the annual balance sheet information of Italian firms. One of the main issues in bankruptcy predictions is related to the selection of the best set of indicators. Therefore, our main research question concerns the identification of the determinants of corporate financial distress, comparing the performance of innovative selection techniques. Furthermore, several aspects related to the default risk analysis have been considered, namely the nature of the numerical information and the sample design. The proposed models take in consideration the above-mentioned issues and the empirical results, elaborated on a data set of financial indices expressly derived from annual reports of the industrial firms. These reports provide evidence in favor of our proposal over the traditional ones.</p>
Highlights
In recent years, the interest in the prediction of corporate financial distress has grown together with the global increase of corporate collapses
In this work we aim at developing default risk models for predictions and diagnosis of the risk of bankruptcy, in particular we focus on the variable selection of the best optimal set of predictors
The predictive performance of the developed models has been evaluated in terms of: Correct Classification Rate (CCR); Area under the Receiver Operating Characteristics (ROC) curve (AUC); Accuracy Ratio (AR)
Summary
The interest in the prediction of corporate financial distress has grown together with the global increase of corporate collapses. This has happened due to the consequences of bankruptcy (Riasi, 2015). Since the fundamental paper of Beaver (1966), which proposes for the first time the use of financial indicators as bankruptcy predictors, and the even more essential work of Altman (1968), which extended the previous intuition to a multivariate framework, there have been many contributions in this field Others have compared the performance of static and dynamic models to investigate the impact of time dynamics on both parameter estimations and model performance (Balcaen and Ooghe, 2004; Chava and Jarrow, 2004; Dakovic et al, 2007; Hillegeist et al, 2004)
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