经济与管理

基于KMV模型上市公司信用风险评价——以人工智能行业为例

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  • 1.葛腾飞:马鞍山学院 经济与管理学院,安徽 马鞍山 243100 2.白中帅:安徽大学 经济学院,安徽 合肥 230601 3.徐娟:深圳市赛为智能股份有限公司,广东 深圳 518000
葛腾飞(1984—),男,安徽潜山人,马鞍山学院经济与管理学院副教授,主要从事经济与金融研究。

收稿日期: 2020-06-17

  网络出版日期: 2021-02-02

基金资助

安徽高校人文社会科学研究重点项目(项目编号:SK2019A0793)

Credit Risk Evaluation of Listed Companies Based on KMV Model:Taking the Artificial Intelligence Industry as an Example

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  • 1.GE Teng-fei:School of Economics and Management, Ma'anshan University, Maanshan Anhui 243100 2.BAI Zhong-shuai:School of Economics, Anhui University, Hefei Anhui 230601 3.XU Juan:Shenzhen Sunwin Intelligent Co., Ltd., Shenzhen Guangdong 518000

Received date: 2020-06-17

  Online published: 2021-02-02

摘要

信用风险是上市公司存在的主要风险之一。人工智能行业作为新兴行业得到了投资者和社会广泛关注,其信用风险更是受到公司管理层的关注。以人工智能行业上市公司为研究对象,运用KMV模型进行计量分析,算出违约点与违约距离,进一步推算违约概率,以此作为评判上市公司信用风险状况的估计值,再以中小板、创业板和选取的公司为例,通过横纵向对比分析人工智能行业信用风险现状和存在问题,并提出对策建议。

本文引用格式

葛腾飞, 白中帅, 徐娟 . 基于KMV模型上市公司信用风险评价——以人工智能行业为例[J]. 巢湖学院学报, 2020 , 22(6) : 45 -53 . DOI: 10.12152/j.issn.1672-2868.2020.06.006

Abstract

Credit risk is one of the main risks of listed companies. As an emerging industry, artificial intelligence industry has attracted much attention from investors and society, and its credit risk is emphasized by the company management. This paper takes the listed companies in artificial intelligence industry as the research object, employs KMV model to conduct quantitative analysis, calculates the point of default and the distance of default, and further works out the probability of default, which can be used as the estimated value to evaluate the credit risk status of listed companies. Then, taking the SME board, GEM and selected companies as examples, the paper analyzes the current situation and existing problems of credit risk in artificial intelligence industry through horizontal and vertical comparison, and puts forward countermeasures and suggestions.
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