Non-Performing Loans for Italian Companies: When Time Matters.

What is it about?

Within bank activities, which is normally defined as the joint exercise of savings collection and credit supply, risk-taking is natural, as in many human activities. Among risks related to credit intermediation, credit risk assumes particular importance. It is most simply defined as the potential that a bank borrower or counterparty fails to fulfil correctly at maturity the pecuniary obligations assumed as principal and interest. Whenever this happens, a loan is non-performing. Among the main risk components, the Probability of Default (PD) and the Loss Given Default (LGD) have been the subject of greater interest for research. In this paper, logit model is used to predict both components. Financial ratios are used to estimate the PD. Time of recovery and presence of collateral are used as covariates of the LGD. Here, we confirm that the main driver of economic losses is the bureaucratically encumbered recovery system and the related legal environment. The long time required by Italian bureaucratic procedures, simply put, seems to lower dramatically the chance of recovery from defaulting counterparties.

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Why is it important?

On the classification model, a study made on similar dataset by Phongmekin and Jarumaneeroj(2018)has shown that classification techniques such as logistic regression (LR), decision tree (DT),linear discriminant analysis (LDA) and K-nearest neighbor (KNN), are comparatively good. In addition,they found that LR and LDA are “the most useful classifiers for risk averse investors—as both are not subject to uncertainty due to true positive counting bias”.On thePD, we showed that the ROI and the WC to Asset are the most relevant variables. To check the quality of our results, we complemented the analysis by running the Firth’s penalized likelihood. The latter is a method of addressing issues of separability, small sample sizes and bias of the parameter estimates. Then, we demonstrated that the outcomes are similar.On the LGD, several studies have certainly led to assertion that there are no universally valid models for any type of loans’ technical form Yashkir and Yashkir (2013). If a single solution has notyet been found on the issue of LGD modeling, it is because the loss rate depends on both external macroeconomic factors and individual characteristics of each credit institution. However, those who have tried to incorporate the effects of the economy by adding some macroeconomic variable to the model (see, for example, Bruche and González-Aguado (2010); Bellotti and Crook (2012);Leow et al. (2014)) did not report any significant improvement. For this reason, we share the opinion that LGD estimates should reflect the practice of each individual institution Dahlin and Storkitt (2014). Furthermore, the analysis carried out in this specific case shows how recovery time assumes great importance in determining the degree of loss, more than the presence of collateral to guarantee the credit. To maximize the recovery of a non-performing credit, the time taken to recover the credit should therefore be minimized. This is of great importance for Italy where bureaucracy is cumbersome,judicial system is slow and legal enforcement is inefficacy.