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.

Andreeva, G., Calabrese, R., Osmetti, S. A., A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models, <<EUROPEAN JOURNAL OF OPERATIONAL RESEARCH>>, 2016; 249 (2): 506-516. [doi:10.1016/j.ejor.2015.07.062] [http://hdl.handle.net/10807/75546]

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

Osmetti, Silvia Angela
Ultimo
2016

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.
Inglese
Andreeva, G., Calabrese, R., Osmetti, S. A., A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models, <<EUROPEAN JOURNAL OF OPERATIONAL RESEARCH>>, 2016; 249 (2): 506-516. [doi:10.1016/j.ejor.2015.07.062] [http://hdl.handle.net/10807/75546]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/75546
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