Systemic Sovereign Risk and Asset Prices: Evidence from the CDS Market, Stressed European Economies and Nonlinear Causality Tests

Antonio Blanco-Oliver (), Ana Diéguez, María Oliver-Alfonso () and Nicholas Wilson ()
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Antonio Blanco-Oliver: University of Seville, Spain
María Oliver-Alfonso: University of Seville, Spain
Nicholas Wilson: Leeds University Business School, UK

Czech Journal of Economics and Finance (Finance a uver), 2015, vol. 65, issue 2, 144-166

Abstract: The use of non-parametric methodologies, the introduction of non-financial variables, and the development of models geared towards the homogeneous characteristics of corporate sub-populations have recently experienced a surge of interest in the bankruptcy literature. However, no research on default prediction has yet focused on micro-entities (MEs), despite such firms’ importance in the global economy. This paper builds the first bankruptcy model especially designed for MEs by using a wide set of accounts from 1999 to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs outperform the traditional logistic regression (LR) models. In addition, we also report that, thanks to the introduction of non-financial predictors related to age, the delay in filing accounts, legal action by creditors to recover unpaid debts, and the ownership features of the company, the improvement with respect to the use of solely financial information is 3.6%, which is even higher than the improvement that involves the use of the best ANN (2.6%).

Keywords: bankruptcy models; micro-entities; credit risk; non-financial information; artificial neural network; logistic regression (search for similar items in EconPapers)
JEL-codes: C45 D81 G21 G33 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:fau:fauart:v:65:y:2015:i:2:p:144-166

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