자기연상 학습 신경망과 부호 입력 변수를 이용한 종합주가지수 "왼쪽어깨" 패턴 검출

“Left Shoulder”Detection in Korea Composite Stock Price Index Using an Auto-Associative Neural Network and Sign Variables

We proposed a neural network based “left shoulder”detector. The auto-associative neural network was trained with the “left shoulder”patterns obtained from the Korea Composite Stock Price Index, and then tested out-of-sample with a reasonably good result. A hypothetical investment strategy based on the detector achieved a return of 132% in comparison with 39% return from a buy and hold strategy