All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

ViewsDownloads

Relative Articles

More...

基于数据挖掘的公司债券风险预测分析
Prediction and Analysis of Corporate Bond Risk Based on Data Mining

DOI: 10.12677/ETW.2022.121001, PP. 1-10

Keywords: 公司债券,数据挖掘,XGBoost极端梯度提升,逻辑回归
Corporate Bond
, Data Mining, Extreme Gradient Boosting, Logistic Regression

Full-Text   Cite this paper   Add to My Lib

Abstract:

大数据时代的到来,网络与计算机技术的发展,给债券市场带来了风险预警的新工具。本文从公司内部经营状况的微观风险信息角度出发,利用数据挖掘技术找出影响公司债券到期偿还的关键因素,并建立预测债券违约的方法。研究以XGBoost极端梯度提升算法发现债券是否违约的主要影响因素是营业收入同比增长率和资产负债率,然后建立了债券是否违约的二元logistic回归模型,通过二元logistic回归模型可以进行债券违约的预测。
The advent of the era of big data and the development of the Internet and computer technology have brought new tools for risk early warning to the bond market. From the perspective of micro risk information of the company’s internal operation, this paper uses data mining technology to find out the key factors affecting the maturity of corporate bonds, and establishes a method to predict bond default. Using the extreme gradient boosting algorithm, it is found that the main influencing factors of whether the bond defaults are the year-on-year growth rate of operating revenue and asset liability ratio in this paper. And then we establish a binary logistic regression model of the bond. The binary logistic regression model can predict whether the bond defaults.

References

[1]  东方财富网. 2020年中国债券行业市场现状及发展趋势分析银行间拆借市场交易活跃[EB/OL].
https://baijiahao.baidu.com/s?id=1693106280415074653&wfr=spider&for=pc, 2021-03-02.
[2]  安义宽. 公司债券及其相关品种发展[J]. 经济管理, 2003(11): 6-14.
[3]  丁翠娥. 我国企业债券违约的影响因素及规避措施[J]. 财政监督, 2018(12): 98-102.
[4]  谭文杨. 我国民营企业债券违约原因及启示——“14富贵鸟”案例分析[D]: [硕士学位论文]. 保定: 河北金融学院, 2019.
[5]  Ohlson, J.A. (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18, 109-131.
https://doi.org/10.2307/2490395
[6]  Foxon, T.J., K?hler, J. and Oughton, C. (2015) Innovation for a Low Carbon Economy. Edward Elgar, Cheltenham.
[7]  Collin-Dufresne, P., Goldstein, R.S. and Martin, J.S. (2001) The Determinants of Credit Spread Changes. The Journal of Finance, 56, 2177-2207.
https://doi.org/10.1111/0022-1082.00402
[8]  Bakshi, G., Madan, D.B. and Zhang, F.X. (2001) Investigating the Sources of Default Risk: Lessons from Empirically Evaluating Credit Risk Models. Social Science Electronic Publishing.
https://doi.org/10.2139/ssrn.262673
[9]  Azizpour, S., Giesecke, K. and Schwenkler, G. (2008) Exploring the Sources of Default Clustering. Journal of Financial Economics, 129, 154-183.
https://doi.org/10.1016/j.jfineco.2018.04.008
[10]  周宏, 徐兆铭, 彭丽华, 杨萌萌. 宏观经济不确定性对中国企业债券信用风险的影响——基于2007-2009年月度面板数据[J]. 会计研究, 2011(12): 41-45, 97.
[11]  周宏, 林晚发, 李国平, 王海妹. 信息不对称与企业债券信用风险估价——基于2008-2011年中国企业债券数据[J]. 会计研究, 2012(12): 36-42.
[12]  黄石, 黄长宇. 我国企业债券市场信用风险评级研究[J]. 当代经理人, 2006(21): 457-458.
[13]  曹萍. 基于KMV模型的地方政府债券违约风险分析[J]. 证券市场导报, 2015(8): 39-44.
[14]  曾江洪, 王庄志, 崔晓云. 基于SVM的中小企业集合债券融资个体信用风险度量研究[J]. 中南大学学报: 社会科学版, 2013(2): 5.
[15]  刘慧芳. 基于信用评级的企业债券信用风险预测研究[D]: [硕士学位论文]. 成都: 四川师范大学, 2017.
[16]  沙一诺. 基于数据挖掘的企业债券违约风险预测[D]: [硕士学位论文]. 上海: 上海师范学, 2021.
https://doi.org/10.27312/d.cnki.gshsu.2021.002194
[17]  程照星. 数据挖掘在电信企业客户细分中的应用[D]: [硕士学位论文]. 重庆: 重庆大学, 2004.
[18]  王言, 周绍妮, 石凯. 国有企业并购风险预警及其影响因素研究——基于数据挖掘和XGBoost算法的分析[J]. 大连理工大学学报(社会科学版), 2021, 42(3): 46-57.
[19]  张孟迪. 基于Logistic回归和XGBoost的银行信用卡客户流失预测[D]: [硕士学位论文]. 济南: 山东大学, 2021.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413