Predicting The Stock Closing Price of ISX-Listed Companies Using LSTM - Salim Sallal Al-Hassnawi; Laith Haleem Malik Al-Hchemi (Universitas Mercu Buana, 2022)
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 models training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed 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.