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Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors are having difficulties guessing prices and making investment decisions, especially when statistical techniques have failed to model historical prices. This paper aims to propose an RNNs-based predictive model using the LSTM model for predicting the closing price of four stocks listed on the Iraq Stock Exchange (ISX). The data used are historical closing prices provided by ISX for the period from 2/1/2019 to 24/12/2020. Several attempts were conducted to improve model training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed with high accuracy in predicting closing price movement, despite the Intense volatility of time series. The empirical study concluded the possibility of relying on the RNN-LSTM model in predicting close prices at the ISX as well as decisions making upon. Keywords:Stock, LSTM, Prediction, ANN, RNN, ISX, Pasar keuangan sangat reaktif terhadap peristiwa dan situasi, seperti yang terlihat dari pergerakan nilai saham yang sangat fluktuatif. Akibatnya, investor mengalami kesulitan menebak harga dan membuat keputusan investasi, terutama ketika teknik statistik gagal memodelkan harga historis. Tulisan ini bertujuan untuk mengusulkan model prediksi berbasis RNNs menggunakan model LSTM untuk memprediksi harga penutupan empat saham yang terdaftar di Bursa Efek Irak (BEI). Data yang digunakan adalah harga penutupan historis yang disediakan oleh BEI untuk periode dari 2 Januari 2019 hingga 24 Desember 2020. Beberapa upaya dilakukan untuk meningkatkan pelatihan model dan meminimalkan kesalahan prediksi, karena model dievaluasi menggunakan MSE, RMSE, dan R2. Model memiliki akurasi tinggi dalam memprediksi pergerakan harga penutupan, meskipun volatilitas deret waktu yang intens. Studi empiris menyimpulkan kemungkinan mengandalkan model RNN-LSTM dalam memprediksi harga penutupan di BEI serta pengambilan keputusan. Kata Kunci:Saham, LSTM, Prediksi, ANN, RNN, ISX