Earticle

초록

영어

This study addresses default prediction through ensemble techniques and cost-sensitive learning. It introduces a novel stacking method, focusing on instances with varying misclassification costs. Previous research lacks this comprehensive approach, highlighting a research gap. The proposed technique proves advantageous in terms of economic payoff and performance, offering practical utility. It complies with regulatory monitoring standards without incurring statistical cost penalties. This study demonstrates potential revenue gains even without precise cost ratios. Empirical results using Taiwanese company bankruptcy data (1999-2009, 95 financial ratios) reveal significant outcomes. Firstly, the proposed algorithm markedly improves economic payoff. Secondly, its statistical performance remains unaffected, even considering the dependency on overall prediction errors related to misclassifying defaults. Lastly, the method is computationally efficient, robust across cost scenarios.

목차

Abstract
1.Introduction
2 Methodology
2.1 Stacking Algorithm for Cost-Sensitive Learning
2.2 Economic Performance Measure
3 Empirical Results
3.1 Data
3.2 Results of Economic Performance Evaluation
3.3 Results of Statistical Performance Evaluation
4 Conclusion
Acknowledgments
References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330