Prediction of Stock Price of GCC Islamic Banks using Neural Network

Abstract

Although the future is unpredictable and unclear, there are techniques for predicting future events and reaping investment benefits securely. The use of machine learning and artificial intelligence to forecast stock prices is one such possibility. This study aims to evaluate the predicting ability of two machine learning models (ANN & KNN) to predict the stock prices of GCC Islamic banks with daily stock prices (Open, Close, high & Low) used for a period of ten years; May 4^th, 2012-May 31^st 2022. The trained ANN and KNN models are evaluated using standard strategic indicators: RMSE, MBE, and Accuracy. All the evaluated statistics confirmed that the trained KNN model have better predicting ability compared to ANN with the highest accuracy level of 99.96% and lowest error values. These findings may aid high-frequency traders in improving stock return and covariance prediction in this market.