TY - JOUR
T1 - Simultaneous optimization of artificial neural networks for financial forecasting
AU - Kim, Kyoung Jae
AU - Ahn, Hyunchul
PY - 2012/6
Y1 - 2012/6
N2 - Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.
AB - Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.
KW - Artificial neural networks
KW - Genetic algorithms
KW - Simultaneous optimization
KW - Stock market prediction
UR - http://www.scopus.com/inward/record.url?scp=84862145454&partnerID=8YFLogxK
U2 - 10.1007/s10489-011-0303-2
DO - 10.1007/s10489-011-0303-2
M3 - Article
AN - SCOPUS:84862145454
SN - 0924-669X
VL - 36
SP - 887
EP - 898
JO - Applied Intelligence
JF - Applied Intelligence
IS - 4
ER -