Periods of economic crisis arouse interest in exploring the causes of ﬁrms closure, for preventive and predictive purposes. Failure prediction models are useful tools for bankers to measure the risk of lending and minimise losses, for ﬁrms wishing to evaluate their market position, and, also for investors, asset managers and rating agencies. Quantitative methods to assess the performance of ﬁrms and to predict the bankruptcyevent based on balance sheet indicators are widely used in the credit risk context. Logistic regression and survival analysis techniques based on hazard models are among the methods often employed. AlargedatasetoncapitalcompaniesinItalyfrom2008to2013,including Business Registerdata supplying a complete picture of their legal situation, was used to develop survival models. Training (n = 27286) and holdout (n = 7124) samples were constructed for developing and testing models, respectively. Fixed and time-varying covariates were taken into account and macro-economic variables were included besides the ﬁrms individual ﬁnancial indicators. Furthermore, we considered one- and two-year lagged values of each time-varying covariate. ROC curves that vary as a function of time and AUC up to a given time were used to compare models and obtain global concordance measures
File in questo prodotto:
Non ci sono file associati a questo prodotto.