CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM

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, ISXPasar 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, IS

STOCK CLOSING PRICE PREDICTION OF ISX-LISTED INDUSTRIAL COMPANIES USING ARTIFICIAL NEURAL NETWORKS

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 (3/3/2019) to (31/3/2019). Matlab 2014b was used to run artificial neural networks using nntool software. Model's performance was evaluated using Mean squared error (MSE), Root mean 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 Artificial Neural Networks model to predict stock prices as well as relying on to make decisions