Stock Market Prediction by Non-Linear Combination based on Support Vector Machine Regression Model

Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, stock market forecasting is based on Support Vector Machine (SVM) regression. Firstly, using different linear re...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced research in computer science 2016-11, Vol.7 (7)
Main Authors: Sendhilvel, Lavanya, K, K Ashwin Kumar, Kumar, Ashwin, T, T Niranjan Babu, Babu, Niranjan, Vaishy, Nitish Vaishy Nitish
Format: Article
Language:English
Subjects:
Computer science
Economic forecasting
Forecasting
Genetic algorithms
Genomes
Mutation
Neural networks
Regression analysis
Regression models
Securities markets
Stock exchanges
Support vector machines
Time series
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, stock market forecasting is based on Support Vector Machine (SVM) regression. Firstly, using different linear regression model to extract linear characteristics of stock market system. Secondly, using different Neural Network algorithms to extract nonlinear characteristics of stock market system. Finally, the SVM regression is used for the nonlinear combination forecasting model of different stock exchange prices. Empirical results obtained reveal that the prediction by using the nonlinear combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Those results show that that the proposed nonlinear modeling technique is a very promising approach to financial time series forecasting.
ISSN:0976-5697