Title :
Coal mine enterprises merger and acquisition prediction based on wavelet neural network
Author :
Yuncai Ning ; Xiang Chen
Author_Institution :
Inst. of Manage., China Univ. of Min.&Technol., Beijing, China
Abstract :
Under present merger and reorganization great environment, the merger and acquisition (M&A) results of many China coal enterprises are ineffective. The predictions of coal enterprises M&A efficiency lack related theories support. The paper constructed a coal enterprises M&A efficiency assessment system, built a M&A efficiency prediction model based on wavelet neural network. It investigated the data in 2004-2008 of coal enterprises M&A, applying the model empirically analyzed the coal enterprises M&A efficiency, which results showed the prediction method is feasible and effective.
Keywords :
corporate acquisitions; mining industry; neural nets; wavelet transforms; coal mine enterprises; enterprise acquisition prediction; enterprise merger prediction; wavelet neural network; Artificial neural networks; Computer network management; Conference management; Corporate acquisitions; Demand forecasting; Environmental management; Function approximation; Neural networks; Prediction methods; Predictive models; efficiency prediction; index system; merger and acquisition; wavelet neural network;
Conference_Titel :
Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4606-3
DOI :
10.1109/PACIIA.2009.5406432
