NDLI: Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting

Content Provider	SpringerLink
Author	Lu, Chi Jie
Copyright Year	2012
Abstract	One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting.
Starting Page	2417
Ending Page	2427
Page Count	11
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	7-8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-10-17
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Stock index forecasting Nonlinear independent component analysis Support vector regression Feature extraction Particle swarm optimization Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Incorporating feature selection method into support vector regression for stock index forecasting

Forecasting stock price using Nonlinear independent component analysis and support vector regression

Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting

Financial time series forecasting using LPP and SVM optimized by PSO

Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

FAIPSO: fuzzy adaptive informed particle swarm optimization

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

Forecasting stock returns based on information transmission across global markets using support vector machines

Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting

Similar Documents

Incorporating feature selection method into support vector regression for stock index forecasting

Forecasting stock price using Nonlinear independent component analysis and support vector regression

Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting

Financial time series forecasting using LPP and SVM optimized by PSO

Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

FAIPSO: fuzzy adaptive informed particle swarm optimization

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

Forecasting stock returns based on information transmission across global markets using support vector machines

Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting