Research On Financial Risk Early Warning Of Listed Manufacturing Companies Based On CatBoost Algorith

Due to the accelerated process of economic globalization and the emergence of intelligent manufacturing,the manufacturing industry is facing increasingly fierce competition and changing business environment,and its financial and operational risks have increased,resulting in the number of companies in financial crisis is also increasing.Financial risks are often latent for a long time,and if timely prediction and action can be taken,the losses of investors and enterprises can be minimized.How to prevent financial risks,what factors affect the financial situation of a company,and how to design an effective and applicable early warning model have been the subject of research by many scholars in recent years.Therefore,the key lies in the construction of a financial risk early warning model.Based on a comprehensive review of previous literature and research,this thesis first introduces the definition and theory related to financial risk early warning,and then introduces the relevant principles related to the Catboost algorithm;Second,it details the design scheme regarding the financial risk early warning model,and in the first step,56 ST(specially treated)and 112 non-ST(not specially treated)listed manufacturing companies from 2020-2021 are used as;in the second step,the initially screened financial risk early warning indicators are further screened using normality test,t-test and Mann-Whitney U non-parametric test to establish an efficient and accurate financial risk early warning indicator system for listed manufacturing companies,and in the third step,The third step adopts the CatBoost machine learning algorithm to construct the financial risk early warning model,combined with the five-fold cross The third step adopts the CatBoost machine learning algorithm to construct the financial risk early warning model,combined with the five-fold cross-validation method to ensure the reliability of the model conclusions,and then uses the constructed CatBoost model to predict the financial risk of Shellett,concludes that Shellett will be treated by ST in 2020,and proposes specific countermeasures to prevent and control the financial risk in a targeted manner;finally,conclusions and recommendations.This thesis mainly draws the following conclusions: the financial risk early warning model constructed based on the CatBoost algorithm can effectively warn the financial risk of listed companies in the manufacturing industry,and the accuracy of the model is as high as98.21% in the training set,which indicates that the model has a relatively excellent prediction effect.And,according to the characteristics of the manufacturing industry and the financial situation of Shellett,some specific countermeasures are proposed for Shellett to solve the financial risks and help Shellett get out of the financial difficulties.In order to make the model more applicable,this thesis also provides some model suggestions for enterprises and investors to improve the accuracy of the model.