American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR 2018 2018 Stock Closing Price Prediction of ISX-listed Industrial Companies Using Artificial Neural Networks University of Al-Qadisiyah, Diwaniyah, Iraq Salim Sallal Al Al-Hasnawi University of Al-Qadisiyah, Diwaniyah, Iraq Laith Haleem Al Al-Hchemi Making stock investment decisions is a complex challenge that investors continuously face. When it comes to an uncertain future, making the wrong decision can result in massive losses. The paper aims to develop an artificial neural networks-based model predicting the closing price of top-six traded industrial ISX-listed stocks, which can guide investment decisions. The sample consisted of daily indexes ISX-released from (332019) to (3132019). Matlab 2014b was used to run artificial neural networks using the nntool software. The model's performance was evaluated using Mean squared error (MSE), Root means squared error (RMSE), and R squared. Empirical results demonstrated the ability and efficiency of artificial neural networks to predict closing prices with high accuracy. As a result, we recommended employing the Artificial Neural Networks model to predict stock prices as well as relying on it to make decisions. 2022 2022 47 55 10.54216/AJBOR.060205