NDLI: Stock index prediction: A comparison of MARS, BPN and SVR in an emerging market

Content Provider	IEEE Xplore Digital Library
Author	Chi-Jie Lu Chih-Hsiang Chang Chien-Yu Chen Chih-Chou Chiu Tian-Shyug Lee
Copyright Year	2009
Description	Author affiliation: Graduate Institute of Management, Fu Jen Catholic University, Taipei, Taiwan (Tian-Shyug Lee) \|\| Institute of Business & Management, Ching Yun University, Jung-Li, Taiwan (Chien-Yu Chen) \|\| Department of Industrial Engineering and Management, Ching Yun University, Jung-Li, Taiwan (Chi-Jie Lu) \|\| Institute of Commerce Automatic and Management, National Taipei University of Technology, Taiwan (Chih-Hsiang Chang; Chih-Chou Chiu)
Abstract	Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index prediction. In this study, we compare the forecasting performance of MARS, backpropagation neural network (BPN), support vector regression (SVR), and multiple linear regression (MLR) models in Shanghai B-Share stock index. Experimental results show that MARS outperforms BPN, SVR and MLR in terms of prediction error and prediction accuracy.
Starting Page	2343
Ending Page	2347
File Size	567557
Page Count	5
File Format	PDF
ISBN	9781424448692
DOI	10.1109/IEEM.2009.5373010
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation Predictive models Neural network Multivariate adaptive regression splines Support vector machines Mars Technology management Engineering management Neural networks Stock index prediction Support vector machine classification Economic forecasting Support vector regression Energy management
Content Type	Text
Resource Type	Article

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