Improved prediction of stock market trends in Saudi Arabia by combining artificial neural networks and fuzzy logic techniques

Khalil  A. Alruwaitee; Abdelgalal  O. I. Abaker; Hago  E. M. Ali; Sharaf  Obaid ALI; Abdalla  S. Mahmoud; Azhari  A. Elhag

Khalil A. Alruwaitee Department of Economics and Finance, Business College, Taif University, Saudi Arabia Al-Hawiya 21974, Taif P.O. Box 888, Saudi Arabia
Abdelgalal O. I. Abaker Applied College, Khamis Mushait, King Khalid University, Abha, Saudi Arabia
Hago E. M. Ali Department of Business Administration, Faculty of Science and Humanity Studies. Sulail, Prince Sattam bin Adalaziz University, Saudi Arabia
Sharaf Obaid ALI Department of Mathematics and Statistics, College of Sciences, Shaqra University, Kingdom of Saudi Arabia, previously College of Computer Science, Alzaeim Alazhari University, Khartoum, Sudan
Abdalla S. Mahmoud Department of Mathematics, University College at Raniah, P.O. Box 11099, Taif University, Taif 21944, Saudi Arabia
Azhari A. Elhag Department of Mathematics and Statistics, College of Science, P.O. Box 11099, Taif University, Taif 21944, Saudi Arabia

Abstract

An integrated prediction model designed for the KSA Stock Exchange utilizes Artificial Neural Networks (ANNs) and Fuzzy Logic approaches. The analysis examines thedaily closin g prices of the Al Rajhi share index in the Saudi stock market index from January 15, 2020, to July 15, 2020, to identify important market moves and patterns throughout this timeframe. The suggested model is rigorously assessed utilizing essential statistical metrics such as R-squared, Mean Absolute Percentage Error(MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The indicators together offer a strong evaluation of the model's accuracy and predictive capability, providing significant insights for investors and stakeholders in navigating the changing Saudi Arabian stock market.