DocumentCode :
456652
Title :
Integration of Genetic Algorithm and Neural Network for Financial Early Warning System: An Example of Taiwanese Banking Industry
Author :
Hsieh, Jih-Chang ; Chang, Pei-Chann ; Chen, Shih-Hsin
Author_Institution :
Dept. of Finance, Vanung Univ., Tao-Yuan
Volume :
1
fYear :
2006
fDate :
Aug. 30 2006-Sept. 1 2006
Firstpage :
562
Lastpage :
565
Abstract :
The applications of genetic algorithms and neural networks to financial early warning systems seem potential in the past works. Therefore genetic algorithm and neural network (GNN) are integrated to build a financial early warning system. An example of Taiwanese banking industry is discussed and the financial ratios of each bank were collected from 1998 to 2002. The performance of GNN is compared with other four early warning systems, namely, case-based reasoning (CBR), backpropagation neural network (BPN), logistic regression analysis (LR), and quadratic discriminant analysis (QDA). The result indicates that the GNN proposed in this research is a little superior to the two soft computing early warning systems (CBR and BPN). The GNN outperforms the statistical early warning systems (LR and QDA) at least 13%
Keywords :
backpropagation; bank data processing; case-based reasoning; genetic algorithms; neural nets; regression analysis; Taiwanese banking industry; backpropagation neural network; case-based reasoning; financial early warning system; genetic algorithm; logistic regression analysis; neural network; quadratic discriminant analysis; Alarm systems; Banking; Biological cells; Computer networks; Finance; Genetic algorithms; Genetic mutations; Logistics; Neural networks; Statistical analysis; Financial early warning system.; Genetic algorithm; Neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
Type :
conf
DOI :
10.1109/ICICIC.2006.103
Filename :
1691862
Link To Document :
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