Title :
Application of Artificial Neural Network to Predict Short-Term Capital Flow
Author_Institution :
Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding, China
Abstract :
The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional Back-Propagation algorithm, the Levenberg-Marquardt algorithm was used to train the neural network. The simulation results indicate that the predictive model can be used to carry out the prediction of short-term capital flow.
Keywords :
backpropagation; feedforward neural nets; finance; Levenberg-Marquardt algorithm; artificial neural network; backpropagation algorithm; short-term capital flow prediction; Application software; Artificial neural networks; Backpropagation algorithms; Biological neural networks; Biological system modeling; Computer network management; Computer science; Crisis management; Neural networks; Predictive models; Back-Propagation algorithm; Levenberg-Marquardt algorithm; artificial neural network; predicting; short-term capital flow;
Conference_Titel :
Research Challenges in Computer Science, 2009. ICRCCS '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3927-0
Electronic_ISBN :
978-1-4244-5410-5
DOI :
10.1109/ICRCCS.2009.39