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Using a and a unique set of Italian non-listed Unlikely to Pay (UTP) positions, that consist in the phase that precedes the insolvency but where it is still possible for the company to succeed in restructuring, this paper aims to analyze the relationships between corporate governance characteristics and financial distress status. We compare the performance of corporate governance variables in predicting corporate defaults, using both the Logit and Random Forest models, which previous researchers have deemed to be the most efficient machine learning techniques. Our results show that the use of corporate governance variables – especially with regards to CEO renewal and stability in the composition of the board of directors – increases the accuracy of the Random Forest technique and influences the success of the turnaround process. This paper also confirms the Random Forest technique’s ability to significantly outperform the Logit model in terms of accuracy.

This study has two purposes: 1 To present an alternative method for the study of events related to bond spreads applicable when only a small number of events is available; 2 To analyse the impact of downgradings and upgradings on the French financial market. A small number of events can render the use of traditional methods based on the analysis of abnormal returns difficult. We suggest examining the stationarity of relative spreads and dating a possible interruption in the series by carrying out tests in increasingly wider time windows. This method has been applied to assess the role of rating agencies in the French financial market. The results obtained are, in general, not only similar to those previously obtained in other markets, but also more accurate. The aggregate analysis shows an absence of reaction for upgradings while downgradings determine reaction on financial markets. However, if we expand the analysis to single issuers we find that downgradings had no relevant effect on financial markets in most cases. Only two issuers (France Telecom and Vivendi), with initially good, but rapidly deteriorating, credit reputation, experienced a significant rise of their spreads. In these cases, financial markets reacted prior to the downgrading by the agency. Tests based only on the analysis of the whole events would have led us, in the case of downgradings, to partially flawed conclusions. (

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The Z-Score model developed by Altman in 1968 is considered one of the more reliable predictors of bankruptcy. In contraposition to the existing literature, the paper aims to investigate the Z’-Score and Z’’-Score ability to predict unlike-to-pay (UTP) loans, which is an event far earlier than insolvency. To investigate this relation, the study uses a unique sample of UTP loans, provided by a major Italian bank, and applies, as a predictive model, the Logit model, well known in academics. Final results confirm that the Z’-Score and the Z’’-Score are able to forecast UTP loans. Furthermore, the findings of the papers reveal the importance of corporate governance variables in predicting financial failures.