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

TL;DR

This study compares predictors of small business failure in Italy and the UK using advanced GEV models, highlighting the importance of non-linear patterns and missing data handling for improved prediction accuracy.

Contribution

It introduces the application of GEV-based models, including BGEVA, to SME failure prediction and compares missing data methods, advancing credit risk modelling techniques.

Findings

BGEVA outperforms logistic regression in predictive accuracy.

Weighs of Evidence method better handles missing data than multiple imputation.

Non-linear models capture SME failure patterns more effectively.

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 logis- tic regression which is a standard approach in credit risk modelling. Some important improvements are investigated. Generalised Extreme Value (GEV) regression is applied to correct for the symmetric link function of the logistic regression. 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 relative 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 and should not be assumed to be missing at random.