Cover Image

Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting

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 d...

Full description

Saved in:
Bibliographic Details
Main Authors: Fathi, A.Y., El-Khodary, I.A., Saafan, M.
Format: Article
Published: Institute of Advanced Engineering and Science 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132360139&doi=10.11591%2fijai.v11.i3.pp851-858&partnerID=40&md5=cc010ef04158e6f96b4a3948924e23e6
http://eprints.utp.edu.my/33516/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.33516
record_format eprints
spelling my.utp.eprints.335162022-09-07T07:19:09Z Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting Fathi, A.Y. El-Khodary, I.A. Saafan, M. 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. © 2022, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132360139&doi=10.11591%2fijai.v11.i3.pp851-858&partnerID=40&md5=cc010ef04158e6f96b4a3948924e23e6 Fathi, A.Y. and El-Khodary, I.A. and Saafan, M. (2022) Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting. IAES International Journal of Artificial Intelligence, 11 (3). pp. 851-858. http://eprints.utp.edu.my/33516/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Fathi, A.Y.
El-Khodary, I.A.
Saafan, M.
spellingShingle Fathi, A.Y.
El-Khodary, I.A.
Saafan, M.
Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
author_facet Fathi, A.Y.
El-Khodary, I.A.
Saafan, M.
author_sort Fathi, A.Y.
title Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
title_short Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
title_full Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
title_fullStr Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
title_full_unstemmed Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
title_sort integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
publisher Institute of Advanced Engineering and Science
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132360139&doi=10.11591%2fijai.v11.i3.pp851-858&partnerID=40&md5=cc010ef04158e6f96b4a3948924e23e6
http://eprints.utp.edu.my/33516/
_version_ 1744356179324174336
score 13.211869

Similar Items