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Understanding corporate default using Random Forest: The role of accounting and market information

Author

Listed:
  • Alessandro Bitetto

    (University of Pavia)

  • Stefano Filomeni

    (University of Essex)

  • Michele Modina

    (University of Molise)

Abstract
Recent evidence highlights the importance of hybrid credit scoring models to evaluate borrowers’ creditworthiness. However, the current hybrid models neglect to consider the role of public-peer market information in addition to accounting information on default prediction. This paper aims to fill this gap in the literature by providing novel evidence on the impact of market information in predicting corporate defaults for unlisted firms. We employ a sample of 10,136 Italian micro-, small-, and mid-sized enterprises (MSMEs) that borrow from 113 cooperative banks from 2012–2014 to examine whether market pricing of public firms adds additional information to accounting measures in predicting default of private firms. Specifically, we estimate the probability of default (PD) of MSMEs using equity price of size-and industry- matched public firms, and then we adopt advanced statistical techniques based on parametric algorithm (Multivariate Adaptive Regression Spline) and non-parametric machine learning model (Random Forest). Moreover, by using Shapley values, we assess the relevance of market information in predicting corporate credit risk. Firstly, we show the predictive power of Merton’s PD on default prediction for unlisted firms. Secondly, we show the increased predictive power of credit risk models that consider both the Merton’s PD and accounting information to assess corporate credit risk. We trust the results of this paper contribute to the current debate on safeguarding the continuity and the resilience of the banking sector. Indeed, banks’ hybrid credit scoring methodologies that also embed market information prove to be successful to assess credit risk of unlisted firms and could be useful for forward-looking financial risk management frameworks

Suggested Citation

  • 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.
    • Handle: RePEc:pav:demwpp:demwp0205
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0205.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Default Risk; Distance to Default; Machine Learning; Merton model; SME; PD; SHAP; Autoencoder; Random Forest; XAI;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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