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

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dc.contributor.authorOh, Kyong Joo-
dc.contributor.authorHan, Ingoo-
dc.date.accessioned2008-08-20T02:01:34Z-
dc.date.available2008-08-20T02:01:34Z-
dc.date.issued2001-
dc.identifier.citationThe Korean Communications in Statistics, Vol. 8, No. 2, 2001, pp. 543-556(14)en
dc.identifier.issn1225-9500-
dc.identifier.urihttp://society.kisti.re.kr/journal/kj_view.jsp?kj=GCGHC8&soc=kss&ndsl=y-
dc.identifier.urihttp://www.kss.or.kr/-
dc.identifier.urihttp://hdl.handle.net/10203/7181-
dc.description.abstractThis 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.en
dc.language.isoen_USen
dc.publisherThe Korean Statistical Societyen
dc.subjectChange-Point Detectionen
dc.subjectStructural Changeen
dc.subjectPettitt Testen
dc.subjectBackpropagation Neural Networksen
dc.titleNeural Network Forecasting Using Data Mining Classifiers Based on Structural Change: Application to Stock Price Indexen
dc.typeArticleen

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