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Statistical merging of rating models

Author

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  • S Figini

    (University of Pavia, Pavia)

  • P Giudici

    (University of Pavia, Pavia)

Abstract

In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.41
    DOI: 10.1057/jors.2010.41
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    References listed on IDEAS

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    1. Duffie, Darrell & Singleton, Kenneth J, 1997. "An Econometric Model of the Term Structure of Interest-Rate Swap Yields," Journal of Finance, American Finance Association, vol. 52(4), pages 1287-1321, September.
    2. Fuertes, Ana-Maria & Kalotychou, Elena, 2006. "Early warning systems for sovereign debt crises: The role of heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1420-1441, November.
    3. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
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    Cited by:

    1. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    2. Avdjiev, S. & Giudici, P. & Spelta, A., 2019. "Measuring contagion risk in international banking," Journal of Financial Stability, Elsevier, vol. 42(C), pages 36-51.
    3. 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.
    4. 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.
    5. Paolo Giudici & Emanuela Raffinetti, 2020. "Lorenz Model Selection," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 754-768, October.
    6. 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.
    7. Silvia Facchinetti & Paolo Giudici & Silvia Angela Osmetti, 2020. "Cyber risk measurement with ordinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 173-185, March.
    8. 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.
    9. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
    10. Dan Cheng & Pasquale Cirillo, 2019. "An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages," Risks, MDPI, vol. 7(3), pages 1-21, July.
    11. Zanin, Luca, 2020. "Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    12. 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.
    13. Silvia Figini & Lijun Gao & Paolo Giudici, 2013. "Bayesian operational risk models," DEM Working Papers Series 047, University of Pavia, Department of Economics and Management.
    14. Silvia Figini & Paolo Giudici, 2013. "Credit risk predictions with Bayesian model averaging," DEM Working Papers Series 034, University of Pavia, Department of Economics and Management.
    15. 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.
    16. Paolo Giudici & Paolo Pagnottoni, 2019. "High Frequency Price Change Spillovers in Bitcoin Markets," Risks, MDPI, vol. 7(4), pages 1-18, November.

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