Comparative Analysis of the Performance of Expert Systems and Machine Learning Models in the Context of the Islamic Stock Market

Abstract

In this article, we will compare the performance of the autoregressive statistical methods of time series ARIMA-GARCH (Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedasticity) and the machine learning methods, mainly ANN (Artificial Neural Networks) and SVM (Support Vector Machines). Different methods are suggested in the literature to enable the prediction of the direction of stock market returns. However, with recent improvements in technology, statistical and mathematical have been the most widely used. For this reason, our article focuses on their comparison to test the diverging results in literature when it comes to comparing their performance. The empirical study will rely on Islamic stock indexes as this area needs more research. Regarding the performance, it is evaluated based on two criteria based on calculating the Mean Absolute Percentage Error and the Root Mean Square Error. Hence, after the analysis of the results, we can confirm that neural networks are efficient compared to other methods.