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

fYear

2003

fDate

20-23 March 2003

Firstpage

417

Lastpage

420

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on

Print_ISBN

0-7803-7654-4

Type

conf

DOI

10.1109/CIFER.2003.1196290

Filename

1196290

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3414599