CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING

Charumathi Balakrishnan () and Mangaiyarkarasi Thiagarajan ()
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Charumathi Balakrishnan: Pondicherry University
Mangaiyarkarasi Thiagarajan: Vels Institute of Science, Technology & Advanced Studies

Bulletin of Monetary Economics and Banking, 2021, vol. 24, issue Special Issue, 107-128

Abstract: We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

Keywords: Credit risk modelling; Credit rating prediction; Emerging market score model; Machine learning; Indian debt market (search for similar items in EconPapers)
JEL-codes: G24 G32 G33 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:idn:journl:v:24:y:2021:i:sph:p:107-128

DOI: 10.21098/bemp.v24i0.1401

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