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Bayesian credit ratings: A random forest alternative approach

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  • Imad Bou-Hamad

Abstract

Cerciello and Giudici (2014) proposed a Bayesian approach to improve the ordinal variable selection in credit rating assessment. However, no comparison has been made with other methods and the predictive power was not tested. This study proposes an integrated framework of random forest (RF)-based methods and Bayesian model averaging (BMA) to validate and investigate the ordinal variable importance in evaluating credit risk and predicting default in greater depth. The proposed approach was superior to the Cerciello and Giudici method in terms of predictive accuracy and interpretability when applied to a European credit risk database.

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  • Imad Bou-Hamad, 2017. "Bayesian credit ratings: A random forest alternative approach," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(15), pages 7289-7300, August.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:15:p:7289-7300
    DOI: 10.1080/03610926.2016.1148730
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    Cited by:

    1. Bou-Hamad, Imad & Jamali, Ibrahim, 2020. "Forecasting financial time-series using data mining models: A simulation study," Research in International Business and Finance, Elsevier, vol. 51(C).
    2. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.

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