Improving Stock Price Prediction Using Combining Forecasts Methods

Abstract

This study presents an outcome of pursuing better and effective forecasting methods. The study primarily focuses on the effective use of divide-and-conquer strategy with Empirical Mode Decomposition or briefly EMD algorithm. We used two different statistical methods to forecast the high-frequency EMD components and the low-frequency EMD components. With two statistical forecasting methods, ARIMA (Autoregressive Integrated Moving Average) and EWMA (Exponentially Weighted Moving Average), we investigated two possible and potential hybrid methods: EMD-ARIMA-EWMA, EMD-EWMA-ARIMA based on high and low-frequency components. We experimented with these methods and compared their empirical results with four other forecasting methods using five stock market daily closing prices from the S&P/TSX 60 Index of Toronto Stock Exchange. This study found better forecasting accuracy from EMD-ARIMA-EWMA than ARIMA, EWMA base methods and EMD-ARIMA as well as EMD-EWMA hybrid methods. Therefore, we believe frequency-based effective method selection in EMD-based hybridization deserves more research investigation for better forecasting accuracy.

Keywords

• EMD
• combining forecasts
• time-series analytics
• ARIMA
• EWMA