IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1412.5351.html
   My bibliography  Save this paper

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

Author

Listed:
  • Galina Andreeva
  • Raffaella Calabrese
  • Silvia Angela Osmetti

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.

Suggested Citation

  • Galina Andreeva & Raffaella Calabrese & Silvia Angela Osmetti, 2014. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," Papers 1412.5351, arXiv.org.
  • Handle: RePEc:arx:papers:1412.5351
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1412.5351
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    2. Raffaella Calabrese & Silvia Angela Osmetti, 2015. "Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 230-239, April.
    3. Daniel Berg, 2007. "Bankruptcy prediction by generalized additive models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 129-143, March.
    4. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    5. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    6. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    7. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    8. Raffaella Calabrese & Silvia Angela Osmetti, 2013. "Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1172-1188, June.
    9. Dimitra Michala & Theoharry Grammatikos & Sara Ferreira Filipe, 2013. "Forecasting distress in European SME portfolios," LSF Research Working Paper Series 13-2, Luxembourg School of Finance, University of Luxembourg.
    10. S Figini & P Giudici, 2011. "Statistical merging of rating models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1067-1074, June.
    11. Raffaella Calabrese & Giampiero Marra & Silvia Angela Osmetti, 2016. "Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 604-615, April.
    12. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    13. Jane W. Lu & Paul W. Beamish, 2001. "The internationalization and performance of SMEs," Strategic Management Journal, Wiley Blackwell, vol. 22(6‐7), pages 565-586, June.
    14. Michel Dietsch, 2004. "Should SME exposures be treated as retail or corporate exposures: a comparative analysis of probabilities of default and assets correlations in French and German SMEs," ULB Institutional Repository 2013/14164, ULB -- Universite Libre de Bruxelles.
    15. S-M Lin & J Ansell & G Andreeva, 2012. "Predicting default of a small business using different definitions of financial distress," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(4), pages 539-548, April.
    16. Banasik, John & Crook, Jonathan, 2007. "Reject inference, augmentation, and sample selection," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1582-1594, December.
    17. J Banasik & J Crook & L Thomas, 2003. "Sample selection bias in credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 822-832, August.
    18. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    19. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    20. Dietsch, Michel & Petey, Joel, 2004. "Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 773-788, April.
    21. Dimitra Michala & Theoharry Grammatikos & Sara Ferreira Filipe, 2013. "Forecasting distress in European SME portfolios," DEM Discussion Paper Series 13-2, Department of Economics at the University of Luxembourg.
    22. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    23. Paul Orton & Jake Ansell & Galina Andreeva, 2015. "Exploring the performance of small- and medium-sized enterprises through the credit crunch," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 657-663, April.
    24. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    25. Fantazzini, Dean & DeGiuli, Maria Elena & Figini, Silvia & Giudici, Paolo, 2009. "Enhanced credit default models for heterogeneous SME segments," Journal of Financial Transformation, Capco Institute, vol. 25, pages 31-39.
    26. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Calabrese, Raffaella, 2023. "Contagion effects of UK small business failures: A spatial hierarchical autoregressive model for binary data," European Journal of Operational Research, Elsevier, vol. 305(2), pages 989-997.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Crosato, Lisa & Domenech, Josep & Liberati, Caterina, 2021. "Predicting SME’s default: Are their websites informative?," Economics Letters, Elsevier, vol. 204(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    2. 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.
    3. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    4. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    5. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    6. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    7. Tingqiang Chen & Suyang Wang, 2023. "Incomplete information model of credit default of micro and small enterprises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2956-2974, July.
    8. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    9. 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.
    10. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
    11. Dimitra Michala & Theoharry Grammatikos & Sara Ferreira Filipe, 2013. "Forecasting distress in European SME portfolios," DEM Discussion Paper Series 13-2, Department of Economics at the University of Luxembourg.
    12. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    13. Michal Karas & Mária Režòáková, 2023. "A novel approach to estimating the debt capacity of European SMEs," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 551-581, June.
    14. Corazza, Marco & Funari, Stefania & Gusso, Riccardo, 2016. "Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 1-26.
    15. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2017. "PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs," Working Papers 04, Department of Management, Università Ca' Foscari Venezia.
    16. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    17. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    18. Michal Karas & Mária Režòáková, 2021. "The role of financial constraint factors in predicting SME default," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(4), pages 859-883, December.
    19. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    20. K.K. Jain & P.K. Gupta & Sanjiv Mittal, 2011. "Logistic Predictive Model for SMEs Financing in India," Vision, , vol. 15(4), pages 331-346, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1412.5351. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.