DocumentCode :
3283163
Title :
The Case Study of Adaptive Network-Based Fuzzy Inference System Modeling for TAIEX Prediction
Author :
Min-Hsuan Fan ; Mu-Yen Chen ; Hui-Feng Huang ; Tai-Ying Huang
Author_Institution :
Dept. of Inf. Manage., Nat. Taichung Univ. of Sci. & Technol., Taichung, Taiwan
fYear :
2012
fDate :
25-28 Aug. 2012
Firstpage :
75
Lastpage :
78
Abstract :
The purpose of this research is to investigate the relation between international stock markets and Taiwan´s stock market, and use the statistical method to identify international markets of high correlation with Taiwan´s stock market as the input parameters of the ANFIS (Adaptive Network-based Fuzzy Inference System) model to improve the forecasting accuracy. This paper collects dataset in the period of years 2000~2010 from 13 countries including Japan, Singapore, Malaysia of high correlation with Taiwan. to prove the forecasting effectiveness of the model, this paper compares the proposed model with models proposed in other studies. the experimental results suggest that, the total average RMSE of the proposed model is lower as compared with other models. the contribution of this research is to integrate the statistical method and ANFIS model to effectively enhance the forecasting accuracy for TAIEX stock price prediction.
Keywords :
forecasting theory; fuzzy reasoning; fuzzy set theory; statistical analysis; stock markets; ANFIS model; TAIEX stock price prediction; Taiwan stock market; adaptive network-based fuzzy inference system modeling; forecasting accuracy; international markets; international stock markets; statistical method; total average RMSE; Adaptation models; Artificial neural networks; Data models; Forecasting; Indexes; Predictive models; Stock markets; Adaptive Network-based Fuzzy Inference System; Fuzzy sets; Neural networks; Stock Price Prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on
Conference_Location :
Kitakushu
Print_ISBN :
978-1-4673-2138-9
Type :
conf
DOI :
10.1109/ICGEC.2012.141
Filename :
6457254
Link To Document :
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