Title :
Empirical analysis of the financial risk in the coal industry
Author :
Shi, Jinfa ; Jiao, Hejun
Author_Institution :
Zhengzhou Inst. of Aeronaut. Ind. Manage., Zhengzhou, China
Abstract :
The financial pre-warning has an important bearing on the survival and development of an enterprise. Aimed at the character of the coal industry, the least squares support vector machine prediction model is given based on the principle of the statistical learning theory and structural risk minimization. The result is given that the forecasting model is effective and offers a new method to forecast the financial risk.
Keywords :
financial management; learning (artificial intelligence); least squares approximations; mining industry; risk analysis; support vector machines; coal industry; empirical analysis; financial prewarning; financial risk; forecasting model; least squares support vector machine prediction model; statistical learning theory; structural risk minimization; Industries; Security; Support vector machines; coal industry; early warning analysis; least squares support vector machine;
Conference_Titel :
Service Operations, Logistics, and Informatics (SOLI), 2011 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-0573-1
DOI :
10.1109/SOLI.2011.5986531
