Optimizing Stock Market Price Prediction using a Hybrid Approach Based on HP Filter and Support Vector Regression

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.