Record Details

A Hybrid Model of Neural Networks and Genetic Algorithms for Prediction of the Stock Market Price: Case Study of Palestine

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title A Hybrid Model of Neural Networks and Genetic Algorithms for Prediction of the Stock Market Price: Case Study of Palestine
 
Creator AlQasrawi, Lama
Awad, Mohammed
Hodrob, Rami
 
Subject Forecasting
Genetic algorithms
Levenberg marquardt algorithm
Multilayer perceptron NNs
Recurrent NNs
 
Description 432-444
Accurate stock market predictions are critical to investor protection and economic growth. This study is the first of its kind
to anticipate Palestinian stock market values using artificial intelligence models. In this paper, an improved hybrid model is
given that combines multilayer perceptron neural networks with genetic algorithms to predict the state of the Palestinian stock
market using the Al-Quds Index as the major indicator (MLPNNs-GAs). Furthermore, the stock values of the three largest
Palestinian companies will be forecast using their stock market data. The rationale for merging artificial neural networks
(ANNs) and genetic algorithms (GAs) stem from the fact that stock price data bear highly volatile and nonlinear features. The
undiscovered patterns of relationships in the input and output data can be explored by artificial neural networks. The weights for
the NNs are optimized using genetic algorithms (GAs), which determine the optimal weights based on performance and bestpredicted
minimal mean square error (MSE) value. Recurrent neural networks with Levenberg-Marquardt (RNNs-LM) and
MLPNNs-LM, two more classic models of various neural network techniques, were used to compare the prediction
performance of the proposed model in terms of mean square error. The experimental results show that, with MSEs of 0.0011 for
the Al-Quds Index, 0.0021 for the Bank of Palestine, 0.001 for Palte, and 0.0006 for Padico, the recommended hybrid model
MLPNNs-GAs outperforms other models in terms of closing price predictions. It has been shown that the MLPNNs-GAs model
may give stock market investors reliable and accurate tools for making forecasts; as a result, MLPNNs-GAs is advised as an
effective model for the prediction of nonlinear financial time series data.
 
Date 2024-04-09T11:17:54Z
2024-04-09T11:17:54Z
2024-04
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63725
https://doi.org/10.56042/jsir.v83i4.5559
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.83(4) [April 2024]