A novel hybrid model based on Hodrick\u2013Prescott filter and support vector regression algorithm for optimizing stock market price prediction

Meryem Ouahilal,Mohamed Chahhou,Mohammed El Mohajir,Badr Eddine El Mohajir

doi:10.1186/s40537-017-0092-5

Meryem Ouahilal, Mohamed Chahhou + Show 2 more

Open Access

https://doi.org/10.1186/s40537-017-0092-5

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Abstract

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.

Highlights

This paper addresses the issue of predicting stock market price in financial time series
The significance and novelty of this paper are summarized as follows: A novel efficient algorithm for stock market price prediction We develop an efficient algorithm for predicting stock market price based on machine learning and noise filtering techniques
We propose a novel hybrid approach based on the combination of the Hodrick–Prescott filter (HP) and the Support Vector Regression algorithm (SVR)

Summary

Introduction

This paper addresses the issue of predicting stock market price in financial time series. We focus on the closing price which is the most up-to-date valuation of a security until trading commences again on the trading day. The closing prices provide a useful marker for investors to evaluate changes in stock market prices over time. A financial time series consists of various components equivalent to short-term irregular and seasonal variations, a medium-term business cycle, and long-term trend movement. Most macroeconomic analysis is concerned with a medium-term business cycle and long-term trend movement. These fundamental movements are hidden in the original financial data because of multiple irregular and seasonal variations are dominant in the data [1]

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