Paper Info
Title
Forecasting KOSPI Using a Neural Network with Weighted Fuzzy Membership Functions and Technical Indicators
Abstract
This paper presents a methodology to forecast the direction of change in the daily Korea composite stock price index (KOSPI) by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and thirteen numbers of input features that are derived by overbought conditions and oversold conditions of three numbers of technical indicators. This paper consists of three steps for forecasting the direction of change in the daily KOSPI. In the first step, three numbers of technical indicators are selected to preprocess the daily KOSPI. In the second step, thirteen numbers of input features are derived by overbought conditions and oversold conditions of three numbers of technical indicators. In the final step, NEWFM classifies the next day's direction of change in the daily KOSPI using thirteen numbers of input features that are produced in the second step. The total number of samples is 2928 trading days, from January 1989 to December 1998. About 80% of the whole trading days is used for training and 20% for testing. The performance result of NEWFM for the direction of change in the daily KOSPI is 58.86%.
Year
DOI
Venue
2009
10.1007/978-3-642-10467-1_28
PCM
Keywords
Field
DocType
daily kospi,technical indicator,thirteen number,weighted fuzzy membership function,weighted fuzzy membership functions,input feature,final step,oversold condition,daily korea,trading day,neural network,forecasting kospi,technical indicators,fuzzy rule,fuzzy neural network,indexation,feature extraction
Data mining,Stock price,Pattern recognition,Computer science,Fuzzy logic,Technical indicator,Feature extraction,Artificial intelligence,Artificial neural network
Conference
Volume
ISSN
Citations 
5879
0302-9743
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Sang-hong Lee17211.96
Dongkun Shin2122667.83
Joon S. Lim39912.15