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

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 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.

The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach

“…The BGEVA model has been then improved by Calabrese, Marra, and Osmetti (2016) . Several studies ( Andreeva et al, 2016;Calabrese et al, 2016;Calabrese & Osmetti, 2013;2015 ) have shown that the GEV model outperforms the logistic and the probit models even if the percentage of ones in the sample is one per cent.…”

“…If we classify the rare events as ones, the major drawback of these approaches is that they underestimate the probability of binary rare events for values close to one, such as bank defaults or bail-outs ( Calabrese & Osmetti, 2015;King & Zeng, 2001;Wang & Dey, 2010 ). To overcome this limitation, a model has been proposed in the operational research literature ( Andreeva, Calabrese, & Osmetti, 2016;Calabrese & Osmetti, 2013;2015;Marra, Calabrese, & Osmetti, 2014 ) -the Binary Generalised Extreme Value Additive model, BGEVA (GEV model in the parametric form). This approach is particularly suitable for binary rare events data, i.e.…”

Model Design and Implementation of Enterprise Credit Information Based On Data Mining

“…Tsaih et al [2] used fundamental characteristics, bank borrow relationship, personal trade history, and industry factors includ ing business cycles, macroeconomic factors as input features and developed a probit model to evaluate enterprise credit. Similarly, Andreeva, Calabrese, and Osmetti [3] ext racted profitability, leverage, coverage, liquid ity, scale and non-financial information and applied generalized extreme value models to UK and Italian small businesses. Serrano-Cinca, Gut ierrezNieto and Reyes [4] considered financial informat ion and social impact informat ion to cred it modeling, while Fernandes and Artes [5] introduced spatial dependence into credit risk assessment to imp rove the performance of credit scoring.…”

“…As can be seen from literatures, enterprise fundamental characteristics, financial reports, social and environment indicators are involved in enterprise credit risk evaluation [1][2][3][4][5][6][7], and the basic rules, and several sophisticated models are designed and implemented in cred it scoring [8][9][10][11][12][13][14]. Both the novel indicators and the state-of-art models can imp rove the performance of credit risk evaluation.…”

Methodological approaches to forecasting the industrial development based on the simulation of economic agents' expectations

“…In order to perform a factor analysis of the key indicators of a segment, generalized reporting information will not suffice therefore analysts fall upon a specific list of additional data contained in the system of management accounting that constitutes a trade secret (Andreeva G., 2016, Hafid I., 2016.…”