Credit risk management of scientific and technological enterprises based on text mining

Chenggang Li · Qing Liu · Lei Huang

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Financial Distress and Bankruptcy Prediction Credit Risk and Financial Regulations Evaluation and Optimization Models

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.

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References

1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1977Early warning of bank failure 2011Capital Allocation and Timely Accounting Recognition of Economic Losses 2015Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal 2016Artificial Intelligence in Financial Markets 2017Using a naive Bayesian classifier methodology for loan risk assessment 2019The impact of project risk management maturity on performance: Complexity as a moderating variable 2017Credit Risk Assessment Using Machine Learning Algorithms

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