In this paper, we compare the performance of two non-parametric methods of classification, Regression Trees (CART) and the newly Multivariate Adaptive Regression Splines (MARS) models, in forecasting bankruptcy. Models are implemented on a large universe of US banks over a complete market cycle...

Stakeholder theory posits that integrating corporate social responsibility into the business model can enhance a firm's long-term sustainability. Using the social and environmental ratings from MSCI ESG database as a proxy for corporate social responsibility (CSR, hereafter) for the sample...

Efficiency is considered a key factor when evaluating a bank's performance. Moreover, efficiency enhancement is an explicit policy objective in the Single Market Directive of the European Commission. But efficiency improvements may come at the expense of deteriorating bank profits and excessive...

The methods that bankruptcy courts have traditionally used to adjudicate fraudulent transfer claims have at times led to inconsistent, unpredictable, and inadvertently biased outcomes. However, recent legal and financial innovations may aid bankruptcy courts in assessing fraudulent transfer...

This paper analyzes the relationship between the likelihood that Italian firms implement bank debt restructuring and the trade credit. It uses a sample of 2377 Italian firms over the period of 2010–2014. The empirical analysis shows that the amount of payables and the unexpected delay in the...

This paper investigates the role of industry-specific effects and structural properties of intersectoral customer-supplier relations on the corporate default prediction of individual firms. We focus on a large sample of US exchange-listed companies over the period 1997- 2015 and show that...

A credit derivative is a path dependent contingent claim on the aggregate loss in a portfolio of credit sensitive securities. We estimate the value of a credit derivative by Monte Carlo simulation of the affine point process that models the loss. We consider two algorithms that exploit the...

I employ a variety of machine learning techniques to predict corporate bankruptcies. I compare machine learning techniques' predictions with the ones of reduced-form regressions and structural models. To assess the performances of different models, I compute a range of scores both in-sample and...

In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional...

What kind of models do we need to guide us through the next crisis? If past crises are any indication, we need to explore new approaches. During the Great Financial Crisis, the models that existed at the time were of little value because they focused on firm-level interactions and did not...