Neural Network Forecasting Using Data Mining Classifiers Based on Structural Change: Application to Stock Price Index

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This study suggests integrated neural network models for the stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.
Publisher
The Korean Statistical Society
Issue Date
2001
Keywords

Change-Point Detection; Structural Change; Pettitt Test; Backpropagation Neural Networks

Citation

The Korean Communications in Statistics, Vol. 8, No. 2, 2001, pp. 543-556(14)

ISSN
1225-9500
URI
http://hdl.handle.net/10203/7181
Link
http://society.kisti.re.kr/journal/kj_view.jsp?kj=GCGHC8&soc=kss&ndsl=y
http://www.kss.or.kr/
Appears in Collection
KGSF-Conference Papers(학술회의논문)
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