Predicting The Stock Closing Price of ISX-Listed Companies Using LSTM

Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors having difficulties guessing prices making investment decisions, especially when statistical techniques have failed model historical prices. This paper aims propose an RNNs-based predictive using LSTM for predicting closing price four stocks listed on Iraq Stock Exchange (ISX). The data used provided ISX period from 2/1/2019 24/12/2020. Several attempts were conducted improve models training minimize prediction error, evaluated MSE, RMSE, R2. performed high accuracy in movement, despite Intense volatility time series. empirical study concluded possibility relying RNN-LSTM close at well decisions upon.