Bayesian credit ratings: A random forest alternative approach

Imad Bou-Hamad

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 15, 7289-7300

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.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2016.1148730 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:15:p:7289-7300

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2016.1148730

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().