Prediction of the Price for Stock Index Futures using Integrated Artificial Intelligence Techniques with Categorical Preprocessing

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 697
  • Download : 740
Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.
Publisher
한국경영과학회
Issue Date
1997-11
Language
ENG
Description

This article is confirmed to be submitted through the review and edition of the Korean Operations Research and Management Science Society. Please enter the title (Journal/Proceedings), volume, number, and pages properly when citing the article.

Citation

한국경영과학회 1997년도 추계학술대회, pp.105 - 108

URI
http://hdl.handle.net/10203/5050
Appears in Collection
MT-Conference Papers(학술회의논문)
Files in This Item
1997-129.pdf(326.07 kB)Download

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0