Enterprise Risk Identification Based on Text Analysis and Machine Learning
Based on the traditional financial indicators,this paper applies text analysis and natural semantic processing methods to reconstruct the enterprise risk identification index system based on past and future perspectives.Then,it introduces machine learning methods to construct an enterprise risk identification model based on the financial data of listed companies and the textual information of management dis-cussion and analysis as the data source for enterprise risk identification and prediction.The conclusions of the study are as follows:1)By providing additional information,the risk measurement scale can be improved,and a three-dimensional risk identi-fication system that combines temporal sensitivity and emotional insight can more comprehensively and accurately measure and identify business risks.2)Introduces machine learning algorithms to compare the predictive accuracy of the AdaBoost model,Hist Gradient Boosting model,Random Forest model and Bagging model,and finds that the AdaBoost model is optimal,has the best robustness,and can be used for enterprise risk identification and prediction.3)By applying machine learn-ing and SHAP methods to rank the importance of enterprise risk characteristics and analyze the mechanism of enterprise risk identification,the key influencing factors of enterprise risk can be identified,and the impact mechanism of various risk char-acteristics on the enterprise risk identification model can be observed.This study can provide empirical evidence and decision support for the design of enterprise risk identification index system and optimization of risk identification model,as well as promote the high-quality development of enterprises and supply chain security and stability.
text analyticsmachine learningexternal risksupply chain riskfuture risk
NETL
NSTL
万方数据
