@Article{, title={Prediction of Iraqi Stock Exchange using Optimized Based-Neural Network}, author={Ameer Al-Haq Al-Shamery}, journal={Karbala International Journal of Modern Science مجلة كربلاء العالمية للعلوم الحديثة}, volume={7}, number={4}, pages={327-339}, year={2021}, abstract={Stock market prediction is an interesting fnancial topic that has attracted the attention of researchers forthe last years. This paper aims at improving the prediction of the Iraq-Stock-Exchange (ISX) using adeveloped method of feedforward Neural-Networks based on the Quasi-Newton optimization approach.The proposed method reduces the error factor depending on the Jacobian vector and Lagrange multiplier.This improvement has led to accelerating convergence during the learning process. A sample ofcompanies listed on ISX was selected. This includes twenty-six banks for the years from 2010 to 2020. Toevaluate the proposed model, the research fndings are compared with other standard predictiontechniques. It was found that the developed research model outperformed other prediction techniquesaccording to the accuracy and root-mean-secured-error measures.

} }