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

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  • Andreeva, Galina
  • Calabrese, Raffaella
  • Osmetti, Silvia Angela

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.

Suggested Citation

  • Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:506-516
    DOI: 10.1016/j.ejor.2015.07.062
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    Cited by:

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    2. Calabrese, Raffaella & Degl’Innocenti, Marta & Osmetti, Silvia Angela, 2017. "The effectiveness of TARP-CPP on the US banking industry: A new copula-based approach," European Journal of Operational Research, Elsevier, vol. 256(3), pages 1029-1037.
    3. Aneta Ptak-Chmielewska, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    4. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    5. Olga N. Kusakina & Natalia V. Bannikova & Svetlana S. Morkovina & Tatiana N. Litvinova, 2016. "State Stimulation of Development of Small Entrepreneurship in Developing Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 276-284.
    6. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    7. Crosato, Lisa & Domenech, Josep & Liberati, Caterina, 2021. "Predicting SME’s default: Are their websites informative?," Economics Letters, Elsevier, vol. 204(C).
    8. Maté-Sánchez-Val, Mariluz & López-Hernandez, Fernando & Rodriguez Fuentes, Christian Camilo, 2018. "Geographical factors and business failure: An empirical study from the Madrid metropolitan area," Economic Modelling, Elsevier, vol. 74(C), pages 275-283.
    9. Ptak-Chmielewska Aneta, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    10. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    11. Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.
    12. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    13. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.

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