基于文本大数据的企业信用风险评估

doi:10.11959/j.issn.2096-0271.2017006

Abstract

Abstract:

Banks have more text information pertaining to credit risk of enterprises.This information is more complete and standardized compared with that of individuals.Nevertheless,this incremental information of enterprise credit risk,which is contained in documents has not been effectively utilized by banks.The role of text big data on credit risk evaluation through annual reports and commentaries of crowds on networks was explored.It found that both the annual reports and crowds’ commentaries significantly contain the incremental information on credit risk of enterprises.

Key words: text, big data, credit risk, evaluation

CLC Number:

F830

Yang YANG, Yimao ZHOU, Zongfang ZHOU. Credit risk evaluation with text big data from text[J]. Big Data Research, 2017, 3(1): 44-50.

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References 19

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变量	释义
X₁：流动比率	流动资产／流动负债
X₂：速动比率	(流动资产－存货)／流动负债
X₃：资产负债率	负债总额／资产总额
X₄：资产回报率	税后净利润／资产总额
X₅：净利润率	净利润／主营业务收入
X₆：营业利润率	营业利润／全部营业收入
X₇：每股收益率	税后净利润／发行在外的普通股股份数
X₈：存货周转率	营业收入／存货平均余额
X₉：资本周转率	销售收入／股东权益平均余额
X₁₀：销售净现率	经营现金净额／销售收入

独立董事意见一	独立董事意见二
关联交易不会对公司未来财务状况造成风险，不存在损害公司或投资人利益的情形	关联交易有利于公司降低经营风险，对公司未来的发展具有积极意义

分析方法	第一类错误率	第二类错误率	评估准确率
传统Logistic模型	20%	30%	75%
企业年度财务报表	16%	24.5%	79.75%
独立董事关联交易意见	10%	25.5%	82.25%

分析方法	第一类错误率	第二类错误率	评估准确率
传统Logistic模型	20%	30%	75%
网络媒体	8.5%	18.5%	86.75%

Credit risk evaluation with text big data from text

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