Paper Info
Title
Optimizing Stock Market Price Prediction Using A Hybrid Approach Based On Hp Filter And Support Vector Regression
Abstract
Predicting stock prices is an important task of financial time series forecasting, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in recent times to predict the stock price, including regression algorithms which can be useful tools to provide good accuracy of financial time series forecasting. In this paper, we propose a novel hybrid approach which combines Support Vector Regression and Hodrick-Prescott filter in order to optimize the prediction of stock price. To assess the performance of this proposed approach, we have conducted several experiments using Maroc Telecom (IAM) financial time series. It is daily data collected during the period between 2004 and 2016. The experimental results confirm that the proposed model is more powerful in term of predicting stock prices.
Year
Venue
Keywords
2016
2016 4TH IEEE INTERNATIONAL COLLOQUIUM ON INFORMATION SCIENCE AND TECHNOLOGY (CIST)
Stock price prediction, Time series forecasting, Support vector regression, Hodrick-Prescott filter, Decision support
Field
DocType
ISSN
Econometrics,Time series,Stock price,Regression,Computer science,Support vector machine,Hodrick–Prescott filter,Stock market,Market research,Price prediction
Conference
2327-185X
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Meryem Ouahilal100.34
El Mohajir Mohammed263.80
Mohamed Chahhou342.15
Badr Eddine El Mohajir436.77