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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Muhammad Saafan
Universiti Teknologi PETRONAS (UTP)
Computer Science & IT Engineering

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Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting Asmaa Y. Fathi; Ihab A. El-Khodary; Muhammad Saafan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp851-858

Abstract

The main objective of stock market investors is to maximize their gains. As a result, stock price forecasting has not lost interest in recent decades. Nevertheless, stock prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics of specific stock markets can differ significantly between countries and regions, based on size, liquidity, and regulations. Accordingly, it is difficult to predict stock prices that are volatile and noisy. This paper presents a hybrid model combining singular spectrum analysis (SSA) and nonlinear autoregressive neural network (NARNN) to forecast close prices of stocks. The model starts by applying the SSA to decompose the price series into various components. Each component is then used to train a NARNN for future price forecasting. In comparison to the autoregressive integrated moving average (ARIMA) and NARNN models, the SSA-NARNN model performs better, demonstrating the effectiveness of SSA in extracting hidden information and reducing the noise of price series.
Co-Authors Asmaa Y. Fathi Ihab A. El-Khodary