DocumentCode :
2182024
Title :
Applications of Artificial Neural Networks in Financial Economics: A Survey
Author :
Li, Yuhong ; Ma, Weihua
Author_Institution :
Sch. of Econ. & Manage., Shijiazhuang Railway Univ., Shijiazhuang, China
Volume :
1
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
211
Lastpage :
214
Abstract :
This paper is a survey on the application of artificial neural networks in forecasting financial market prices. The objective of this paper is to appraise the potential of using artificial neural networks to predict the financial system, as it is reflected in many relevant articles. It will provide some guidelines and references for the research and implementation. This paper begins with an introduction to the theory of artificial neural networks. Subsequently it focuses on the forecast of stock prices and option pricing based on a non-linear ANN model. It proceeded with a presentation of the application of ANN in predicting exchange rates. The paper then reviewed the theoretical literature on the prediction of banking and financial crisis based on artificial neural networks. In general artificial neural network is a valuable forecast tool in financial economics due to the learning, generalization and nonlinear behavior properties. Finally it identifies a number of important opportunities for future research on the application of neural networks in financial economics.
Keywords :
financial data processing; neural nets; pricing; ANN; artificial neural networks; banking; financial crisis; financial economics; financial market prices; Artificial neural networks; Biological system modeling; Data models; Exchange rates; Forecasting; Predictive models; Pricing; artificial neural networks; exchange rates; financial crisis; forecast; stock prices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2010 International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-8094-4
Type :
conf
DOI :
10.1109/ISCID.2010.70
Filename :
5692701
Link To Document :
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