eprintid: 18823
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/88/23
datestamp: 2024-06-04 14:11:14
lastmod: 2024-06-04 14:11:14
status_changed: 2024-06-04 14:04:10
type: article
metadata_visibility: show
creators_name: Fathi, A.Y.
creators_name: El-Khodary, I.A.
creators_name: Saafan, M.
title: A Hybrid Model Combining Discrete Wavelet Transform and Nonlinear Autoregressive Neural Network for Stock Price Prediction: An Application in the Egyptian Exchange
ispublished: pub
note: cited By 3
abstract: Forecasting stock prices is crucial for successful investment in financial markets. However, it is challenging due to the nonlinearity and high volatility caused by various factors influencing price movements. This paper proposes a hybrid model that integrates the discrete wavelet transform (DWT) with the nonlinear autoregressive neural network (NARNN) to predict stock prices. Following the division of stock prices into training and testing sets, the DWT decomposes the training set into low- and high-frequency components reducing the noise and lessening the data's nonlinearity. Then, the obtained components are used to train the NARNNs. To predict the future components, the model decomposes the preceding available prices at each time step and utilizes the latest eight points as input to the NARNNs. Eventually, NARNNs' outputs are combined to provide the final predicted prices. In previous works, the entire dataset is first decomposed and then partitioned into training and testing sets. This unrealistic approach causes the testing set to inherit information regarding stocks' future performance, leading to optimistic deceptive results. Twenty-four stocks from the Egyptian Exchange (EGX-30) are utilized to validate the proposed model's performance. The DWT-NARNN model is compared against other methods, and the empirical findings show that it performs the best. © 2023 Lavoisier. All rights reserved.
date: 2023
publisher: International Information and Engineering Technology Association
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152204129&doi=10.18280%2fria.370103&partnerID=40&md5=e0bf7f18b1efd6af1afd5546a75cf2d0
id_number: 10.18280/ria.370103
full_text_status: none
publication: Revue d'Intelligence Artificielle
volume: 37
number: 1
pagerange: 15-21
refereed: TRUE
issn: 0992499X
citation:   Fathi, A.Y. and El-Khodary, I.A. and Saafan, M.  (2023) A Hybrid Model Combining Discrete Wavelet Transform and Nonlinear Autoregressive Neural Network for Stock Price Prediction: An Application in the Egyptian Exchange.  Revue d'Intelligence Artificielle, 37 (1).  pp. 15-21.  ISSN 0992499X