A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models

Galina Andreeva, Raffaella Calabrese () and Silvia Angela Osmetti

European Journal of Operational Research, 2016, vol. 249, issue 2, 506-516

Abstract: This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are considered, starting with the logistic regression which is a standard approach in credit risk modelling. Some important improvements are investigated. Generalised Extreme Value (GEV) regression is applied in contrast to the logistic regression in order to produce more conservative estimates of default probability. The assumption of non-linearity is relaxed through application of BGEVA, non-parametric additive model based on the GEV link function. Two methods of handling missing values are compared: multiple imputation and Weights of Evidence (WoE) transformation. The results suggest that the best predictive performance is obtained by BGEVA, thus implying the necessity of taking into account the low volume of defaults and non-linear patterns when modelling SME performance. WoE for the majority of models considered show better prediction as compared to multiple imputation, suggesting that missing values could be informative.

Keywords: Decision support systems; Risk analysis; Credit scoring; Small and Medium Sized Enterprises; Default prediction (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Working Paper: A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:249:y:2016:i:2:p:506-516

DOI: 10.1016/j.ejor.2015.07.062

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