A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in this area to predict the stock market price, including regression algorithms which can be useful tools to provide good performance of financial time series prediction. Support Vector Regression is one of the most powerful algorithms in machine learning. There have been countless successes in utilizing SVR algorithm for stock market prediction. In this paper, we propose a novel hybrid approach based on machine learning and filtering techniques. Our proposed approach 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 real world datasets. The principle objective of this paper is to demonstrate the improvement in predictive performance of stock market and verify the works of our proposed model in comparison with other optimized models. The experimental results confirm that the proposed algorithm constitutes a powerful model for predicting stock market prices.