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Development of the KOSPI (Korea Composite Stock Price Index) forecast model using neural network and statistical methods)  

Lee, Eun-Jin (Dept. of Information, Communications and Electronics Eng., The Catholic University of Korea)
Min, Chul-Hong (Dept. of Computer Science and Eng., The catholic University of Korea)
Kim, Tae-Seon (Dept. of Information, Communications and Electronics Eng., The Catholic University of Korea)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.5, 2008 , pp. 95-101 More about this Journal
Abstract
Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.
Keywords
신경 회로망;회귀분석;종합주가지수;에이전트시스템;
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