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Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks

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Abstract

During the last three decades various models have been proposed by the literature to predict the risk of bankruptcy and of firm insolvency. In this work there is a survey on the methodologies used by the author for the analysis of default risk, taking into account several approaches suggested by the literature. The focus is to analyse the Artificial Neural Networks as a tool for the study of this problem and to verify the ability of classification of these models. Finally, an analysis of variables introduced in the Artificial Neural Network models and some considerations about these.

Suggested Citation

  • Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    • Handle: RePEc:csc:cerisp:200610
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    Cited by:

    1. Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023. "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    2. You Zhu & Chi Xie & Bo Sun & Gang-Jin Wang & Xin-Guo Yan, 2016. "Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models," Sustainability, MDPI, vol. 8(5), pages 1-17, May.
    3. Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2021. "Machine Learning and Credit Risk: Empirical Evidence from SMEs," DEM Working Papers Series 201, University of Pavia, Department of Economics and Management.

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    More about this item

    Keywords

    Artificial Neural Networks; Hybrid neural network models Expert Systems; Default; Bankruptcy; Rating Systems; Credit scoring models;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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