Financial distress prediction of information technology companies based on random forest and SVM model
With the rapid development of information technology companies,they face fierce market competition and high un-certainty.In recent years,there are constantly listed information technology companies falling into financial distress.Therefore,it is very important for investors,companies and market supervision administration to predict the financial distress of listed information technology companies.By using two machine learning algorithms,random forest and support vector machine(SVM),the research takes A-share listed information technology companies as an example to predict the financial distress of the sample companies in the year T,and uses relevant evaluation indicators to cross-compare the prediction effects of each model in different periods.The results show that the above two models which built on the same datasets can predict the financial situation in the T-year with higher accuracy,while the prediction effect of the SVM model is better than that of the random forest,and the closer to the T-year,the bet-ter the prediction effect of the two models will be.
NETL
NSTL
万方数据
