Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Neural Network Forecasting Using Data Mining Classifiers Based on Structural Change: Application to Stock Price Index

  • Oh, Kyong-Joo (Techno-Management Institute, Korea Advanced Institute of Science and Technology) ;
  • Han, Ingoo (Graduate School of Management, Korea Advanced Institute of Science and Technology)
  • Published : 2001.08.01
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Abstract

This study suggests integrated neural network modes for he 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.

Keywords

References

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