Performance Comparison of Neural Networks (MLP, Rbfnn, Ernn, Jrnn) Models for Stock Prices Forecasting to Bank of Palestine

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Performance Comparison of Neural Networks (MLP, Rbfnn, Ernn, Jrnn) Models for Stock Prices Forecasting to Bank of Palestine

Shady I. Altelbany a & Anwar A. Abualhussein b

AL-Azhar University, Faculty of Economics and Administration Sciences

Abstract

This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine  from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1  with minimum forecast measure error.

Muthanna Journal of Administrative and Economic Sciences, 2021, Volume 11, Issue 1, Pages 8-28

DOI:10.52113/6/2021-11-1/8-28

Categories: Uncategorized