Title :
Trend detection using auto-associative neural networks: Intraday KOSPI 200 futures
Author :
Lee, Junmyung ; Cho, Sungzoon ; Baek, Jinwoo
Author_Institution :
Dept. of Ind. Eng., Seoul Nat. Univ., South Korea
Abstract :
This paper reports the results of a new neural network based trend detector. An auto-associative neural network was trained with the "trend" data obtained from the intra-day KOSPI 200 future price. It was then used to predict a trend. Simple investment strategies based on the detector achieved a one-year return of 31.2 points with no leverage.
Keywords :
associative processing; financial data processing; investment; learning (artificial intelligence); neural nets; stock markets; Intraday KOSPI 200 futures; Korea Composite Stock Price Index; auto-associative neural networks; financial engineering; investment; trend detection; Data mining; Detectors; Electronic mail; Industrial engineering; Investments; Neural networks; Pattern analysis; Pattern classification; Pattern recognition; Training data;
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
DOI :
10.1109/CIFER.2003.1196290
