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Bayesian regularized artificial neural networks for the estimation of the probability of default

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  • Sariev, Eduard
  • Germano, Guido

Abstract

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.

Suggested Citation

  • Sariev, Eduard & Germano, Guido, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," LSE Research Online Documents on Economics 101029, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:101029
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    References listed on IDEAS

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    1. Eduard Sariev & Guido Germano, 2019. "An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 404-427, July.
    2. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    3. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    4. 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.
    5. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    6. Iana Liadze & Ray Barrell & Professor E. Philip Davis, 2010. "The impact of global imbalances: Does the current account balance help to predict banking crises in OECD countries?," National Institute of Economic and Social Research (NIESR) Discussion Papers 351, National Institute of Economic and Social Research.
    7. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    8. Shiyi Chen & W. K. Hardle & R. A. Moro, 2011. "Modeling default risk with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 11(1), pages 135-154.
    9. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    10. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1.
    11. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    12. Iana Liadze & Ray Barrell & Professor E. Philip Davis, 2010. "The impact of global imbalances: Does the current account balance help to predict banking crises in OECD countries?," National Institute of Economic and Social Research (NIESR) Discussion Papers 351, National Institute of Economic and Social Research.
    13. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2.
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    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2022. "A data-driven explainable case-based reasoning approach for financial risk detection," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2257-2274, December.
    4. Michael L. Polemis & Mike G. Tsionas, 2023. "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1602-1621, April.
    5. Caplescu Raluca Dana & Panaite Ana-Maria & Pele Daniel Traian & Strat Vasile Alecsandru, 2020. "Will they repay their debt? Identification of borrowers likely to be charged off," Management & Marketing, Sciendo, vol. 15(3), pages 393-409, September.
    6. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    7. Jaewon Park & Minsoo Shin & Wookjae Heo, 2021. "Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms," Risks, MDPI, vol. 9(2), pages 1-19, February.

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    More about this item

    Keywords

    Artificial neural networks; Bayesian regularization; Credit risk; Probability of default;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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