Non-tradable shares pricing and optimal default point based on hybrid KMV models: Evidence from China

作者：

Highlights：

• Particle swarm optimization is used to improve the KMV model.

• Particle swarm optimization outperforms genetic algorithm in this study.

• The discount on non-tradable shares is calculated by the PSO-KMV model.

• The FC-PSO-KMV model uses fuzzy clustering to obtain a specific default point.

摘要

•Particle swarm optimization is used to improve the KMV model.•Particle swarm optimization outperforms genetic algorithm in this study.•The discount on non-tradable shares is calculated by the PSO-KMV model.•The FC-PSO-KMV model uses fuzzy clustering to obtain a specific default point.

论文关键词：KMV model,Default prediction,Non-tradable shares pricing,Particle swarm optimization,Fuzzy clustering

论文评审过程：Received 16 November 2015, Revised 17 July 2016, Accepted 19 July 2016, Available online 27 July 2016, Version of Record 29 September 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.07.028