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

PREDICTION STOCK MARKET PRICE USING A HYBRID MODEL ALGORITHM

Abstract

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 best-predicted 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.