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All Journal JURISMA: Jurnal Riset Bisnis & Manajemen JIKA: Jurnal Ilmu Keuangan dan Perbankan
Al-Hasnawi, Salim Sallal
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM Al-Hasnawi, Salim Sallal; Al-Hchemi*, Laith Haleem
JURISMA : Jurnal Riset Bisnis & Manajemen Vol. 12 No. 2: Oktober 2022
Publisher : Program Studi Manajemen, Fakultas Ekonomi dan Bisnis, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jurisma.v12i2.8103

Abstract

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
STOCK CLOSING PRICE PREDICTION OF ISX-LISTED INDUSTRIAL COMPANIES USING ARTIFICIAL NEURAL NETWORKS Al-Hasnawi, Salim Sallal; Al-Hchemi, Laith Haleem
Jurnal Ilmu Keuangan dan Perbankan (JIKA) Vol. 11 No. 2: Juni 2022
Publisher : Program Studi Keuangan & Perbankan, Fakultas Ekonomi dan Bisnis, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jika.v11i2.7114

Abstract

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.
Co-Authors Al-Hchemi*, Laith Haleem Al-Hchemi, Laith Haleem