Earticle

초록

영어

In the default prediction problem, thecost from thefailure of forecasting defaults is much biggerthan that of forecastingnon-defaults. Thecost asymmetryis deeperin the corporatedefaultpredictionthan theretailas corporateloan portfolios arenot granular. However, thetwo typesof costs aretreatedequallyin generalas defaultpredictionmodels areusually estimatedto minimizepredictionerrorsor maximizestatistical performance. This practicemight not fulfill thegoal of risk managementto minimizeeconomiclosses. To mitigatethis issue, this study apply cost-sensitivelearningapproach to defaultprediction, which minimizeseconomiccosts insteadof statistical errors. Wedefineeconomic costs and testthemfor various levelsof thecost asymmetryby employingLogistic regression, XGBoost, and LightGBM. As a resultof empiricalexperimentswith Taiwaneseand Polish corporate default data, we first find that the proposed cost-sensitivemodels are superiorto thecost-insensitivecounterpartsin termsof economiccost, mostly regardless of thecost asymmetryscenarios. Secondly, neverthele,ssthedecreasesin thestatistical performanceare relativelysmall – economic costs decrease24.6% at theexpenseof the decrease in AUC of 4.6% on average. This suggests that financial firms can adopt the proposed default prediction models without violating the regulatory requirement on model quality. Lastly, we find that the features of high prediction power in the cost-sensitive and insensitive models are different, which has an important implication for credit monitoring.

목차

Abstract
1 Introduction
2 Methodology
2.1 Economic costs of the default prediction model
2.2 Cost-sensitive Logistic model
2.3 Cost-sensitive gradient-boosting model
2.4 Performance measures
2.5 Threshold to discriminate between a default and a normal
3 Empirical results
3.1 Data
3.2 The results of the cost-sensitive Logistic model
3.3 The results of cost-sensitive XGBoost model
3.4 The results of cost-sensitive LightGBM
3.5 Features analysis of the cost-sensitive model
4 Conclusion
References
Acknowledgements

키워드

저자

• Chan Park [ Ph.D. Candidate , Departmentof Financial Technology Convergence, Soongsil University ] First Author
• Seungyoo Jeon [ Ph.D. Candidate , Departmentof Financial Technology Convergence, Soongsil University ] Co -Author
• Kisung Yang [ Assistant Professor, School of Finance, Soongsil University ] Corresponding Author

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  한국재무학회 학술대회

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  2006~2024

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  KDC 325 DDC 330