Title :
Comparison of the prediction effect between the Logistic Regressive model and SVM model
Author :
Sijia, Li ; Lan, Tan ; Yu, Zhuang ; Xiuliang, Yu
Author_Institution :
City Coll., Zhejiang Univ., Hangzhou, China
Abstract :
Financial crises forewarning has important practical significance both for the investors and for the lenders. This paper uses the financial forewarning models, including the Logistic Regressive model and SVM model, to verify the feasibility of the short-term forecast for the financial situation of enterprises. And the paper also gives comparisons between these two models. The results of the study suggest that these two models are both feasible, and the SVM model can achieve better forecasting effects than the Logistic Regressive model.
Keywords :
economic cycles; forecasting theory; regression analysis; support vector machines; SVM model; financial crises; financial forewarning models; investors; logistic regressive model; prediction effect; short-term forecast; Biological system modeling; Companies; Estimation; Logistics; Predictive models; Support vector machines; Training; Credit Risk Management; Logistic Regressive Model; SVM (Supporting Vector Machine) Model; financial crisis forewarning; prediction effect;
Conference_Titel :
Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-6927-7
DOI :
10.1109/ICIFE.2010.5609308