We aim to provide a predictive model, specifically designed for the Italian economy, which classifies solvent and insolvent firms one year in advance, using AIDA Bureau van Dijk dataset from 2007 to 2015. We apply a full battery of bankruptcy forecasting models, including both traditional and more sophisticated machine learning techniques, and add to the financial ratios used in the literature a set of industrial/regional variables. We find that XGBoost is the best performer and that industrial/regional variables are important. Moreover, belonging to a district, having a high mark up and a greater market share diminish bankruptcy probability.
|Title of host publication||Working Paper N. 19/3 DIPARTIMENTO DI MATEMATICA PER LE SCIENZE, ECONOMICHE, FINANZIARIE ED ATTUARIALI|
|Number of pages||39|
|Publication status||Published - 2019|
- firm distress analysis
- industrial variables
- logistic regression
- machine learning