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

Alessandro Bitetto (), Stefano Filomeni () and Michele Modina
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Alessandro Bitetto: University of Pavia
Stefano Filomeni: University of Essex
Michele Modina: University of Molise

No 205, DEM Working Papers Series from University of Pavia, Department of Economics and Management

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

Keywords: Default Risk; Distance to Default; Machine Learning; Merton model; SME; PD; SHAP; Autoencoder; Random Forest; XAI (search for similar items in EconPapers)
JEL-codes: C52 C53 D82 D83 G21 G22 (search for similar items in EconPapers)
Pages: 50
Date: 2021-10
New Economics Papers: this item is included in nep-acc, nep-ban, nep-big, nep-cfn, nep-cmp and nep-rmg
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