Implications of macroeconomic conditions on Romanian portfolio credit risk. A cost-sensitive ensemble learning methods comparison
Bibliographic Data
| ID | 15066548 |
|---|---|
| Authors | Ana-Maria Sandica (Faculty of Finance and Banking, The Bucharest University of Economic Studies, Bucharest, Romania), Alexandra Fratila (Bucharest University of Economic Studies, corresponding author) |
| Year | 2022 |
| Volume | 35 |
| Issue | 1 |
| Pages | 3571-3590 |
| Publication date | 2022-12-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Research-Ekonomska Istraživanja (JOURNAL) |
| Journal identifiers | ISSN: 1331-677X • E-ISSN: 1848-9664 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1331677x.2021.1997625 |
| OpenAlex | W3214003693 |
| Language | EN |
| References cited | 28 |
Credit risk assessment represents a key instrument in the decision-making of the banking and financial institutions. In this article, we present a framework for credit risk strategies to improve portfolio efficiency under a change of macroeconomic regime. The aim is to compare the accuracy of several ensemble methods (AdaBoost, Logit Boost, Gentle Boost and Random Forest) on a default retail Romanian loan portfolio under different risk adversity scenarios, a priori and posteriori the financial distress. Using cost-sensitive ensemble learning models, we concluded that regime-based credit strategy can offer a better alternative in both terms of loss allocated to the strategy as well as defaulters’ classification accuracy
A priori and a posteriori · Actuarial science · AdaBoost · Credit risk · Econometrics · Economics · Ensemble learning · Loan · Machine learning · Portfolio · Support vector machine · Banking stability, regulation, efficiency · Computer Science · Credit Risk and Financial Regulations · Financial Distress and Bankruptcy Prediction · Finance
| Highly cited | No |
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