Forecasting Short-Term KOSPI using Wavelet Transforms and Fuzzy Neural Network

웨이블릿 변환과 퍼지 신경망을 이용한 단기 KOSPI 예측

  • 신동근 (삼육대학교 컴퓨터학부) ;
  • 정경용 (상지대학교 컴퓨터정보공학부)
  • Received : 2011.03.29
  • Accepted : 2011.04.07
  • Published : 2011.06.28


The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.


Short-Term Trend;Fuzzy Neural Networks;Wavelet Transforms;Feature Selection


Supported by : 삼육대학교


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