웨이블릿 변환과 퍼지 신경망을 이용한 단기 KOSPI 예측

국문 초록 (Abstract)

KOSPI는 정치 및 경제를 포함한 다양한 요소에 영향을 받는 관계로 정확한 단기 KOSPI 예측 방법론 개발은 매우 어려운 문제로 여겨지고 있다. 본 논문에서는 가중 퍼지소속함수 기반 신경망(NEWFM; neural network with weighted fuzzy membership functions)의 특징 추출기법을 사용하여 5일 동안의 주가 단기추세를 예측하는 방안을 제안한다. 비중복면적 분산 측정법에 의해 중요도가 가장 낮은 특징입력을 하나씩 제거하면서 최소의 특징입력을 선택한다. 특징입력으로써 기술지표를 이용하여 얻은 데이터를 웨이블릿 변환을 이용하여 39개의 계수들을 추출한다. 이들 39개의 특징입력 중 비중복면적 분산측정법에 의해서 추출된 12개의 계수가 사용된다. 제안된 방법에서는 민감도가 72.79%, 특이도가 74.76%, 정확도가 73.84%를 나타낸다.

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• 요약
• Abstract
• Ⅰ. 서론
• Ⅱ. 주가 단기추세 예측 모델의 설계
• Ⅲ. 단기 KOSPI 예측을 위해 사용한 퍼지 신경망
• Ⅳ. 실험 결과
• Ⅴ. 결론
• 참고문헌
• 저자소개

참고문헌 (Reference)

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