Study on the Optimization of Default Point of China Listed Company by using Genetic Algorithm KMV Model

DOI

10.2991/meici-17.2017.70How to use a DOI?

Keywords

KMV Model; Genetic Algorithm; Fitness Function; Distance-to-Default

Abstract

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.

Copyright

© 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/).

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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  -