Study on the Optimization of Default Point of China Listed Company by using Genetic Algorithm KMV Model
- 10.2991/meici-17.2017.70How to use a DOI?
- KMV Model; Genetic Algorithm; Fitness Function; Distance-to-Default
Objective of this paper is applying KMV Credit Risk Model to the credit assessment of China listed companies, the KMV Model needs to be modified in combination with the characteristics of listed companies, and setting an accurate default point is crucial. This paper uses several methods including Genetic Algorithm, Double Total independent sample t-test. It introduces Genetic Algorithm into KMV Model, and takes the Double Total independent sample t-test function as the fitness function of genetic algorithm to solve the optimal default point problem of China listed companies. The study shows the conclusion that the KMV model has adaptability in the credit risk assessment of China listed companies, and the optimal default point calculated by genetic algorithm make the KMV model has the strongest distinguishing ability.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jia Lin AU - Yongping Gui PY - 2017/10 DA - 2017/10 TI - Study on the Optimization of Default Point of China Listed Company by using Genetic Algorithm KMV Model BT - Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017) PB - Atlantis Press SP - 364 EP - 373 SN - 1951-6851 UR - https://doi.org/10.2991/meici-17.2017.70 DO - 10.2991/meici-17.2017.70 ID - Lin2017/10 ER -