An Improvement of KMV model: Using a More Accurate Risk Prediction Model to Help Regulating Risk and Alleviating Asymmetric Information in the Financial Market

• 영문명: An Improvement of KMV model: Using a More Accurate Risk Prediction Model to Help Regulating Risk and Alleviating Asymmetric Information in the Financial Market
• 발행기관: 한중경제문화학회
• 저자명: Li Xinlai Wu Chao
• 간행물 정보: 『한중경제문화연구』제25권, 13~23쪽, 전체 11쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2024.02.29

영문 초록

This essay discusses the enhancement of the KMV model to achieve greater accuracy in predicting default risk and mitigating the effects of asymmetric information in the financial market. Due to the existence of the problem of asymmetric information persists, with some market participants possessing more information than others. This imbalance disrupts the normal market operation, complicates financial regulation, and reduces market stability. Rating agencies have made efforts to disclose and predict default risks to provide more information to the market. Still, traditional models’ prediction accuracy has struggled to meet the market’s evolving demands. To address these challenges, this essay analyzes an improved model, the SIZE-PSO-KMV model. This model builds on the KMV model but introduces a differentiation between large and small firms. By doing so, it refines default risk predictions, thereby alleviating information asymmetry. Enhanced accuracy empowers financial regulators to make more informed decisions and helps prevent future financial crises. The SIZE-PSO-KMV model’s validity is established through rigorous testing, including a comparison with other KMV models and out-of-sample tests. The results demonstrate that this model significantly outperforms traditional KMV models in predicting default risk. Additionally, it adapts to the size of firms, acknowledging that large and small firms face distinct default risk profiles.

목차

Ⅰ. Introduction

Ⅱ. Literature review

Ⅲ. Model

Ⅳ. Conclusion

키워드

SIZE-PSO-KMV Model Asymmetric Information Risk Prediction