ABSTRACT

The purpose of this paper is to verify the impact of financial news on corporate credit risk. Web crawler technology is used to obtain the financial news text from Sina Financial News. The text mining technology is utilized to quantify the financial news text. The quantified financial news text combined with financial indicators is used to build the Logistic regression model. The assessment results show that the risk early warning accuracy of the Logistic regression model incorporating both the quantified financial news text and financial indicators is higher than the Logistic regression model only with pure financial indicators.

中文翻译：

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基于文本挖掘的科技企业信用风险管理

摘要

本文旨在验证财经新闻对企业信用风险的影响。网络爬虫技术用于获取新浪财经新闻的财经新闻文本。利用文本挖掘技术对财经新闻文本进行量化。采用量化的财经新闻文本结合财经指标构建Logistic回归模型。评估结果表明，同时包含量化财经新闻文本和财务指标的Logistic回归模型的风险预警准确率高于仅包含纯财务指标的Logistic回归模型。