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ABSTRACT
Title |
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Saudi Arabia Stock Market Prediction Using Neural Network |
Authors |
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Talal Alotaibi, Amril Nazir, Roobaea Alroobaea, Moteb Alotibi, Fasal Alsubeai, Abdullah Alghamdi, Thamer Alsulimani |
Keywords |
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stock market prediction; Saudi Arabia stock prediction; neural network stock prediction. |
Issue Date |
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Jan 2018 |
Abstract |
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Artificial neural networks became one of the most popular methods for forecasting (especially
time-series forecasting) due to their ability to model nonlinear functions. One of the common methods for applying the artificial neural network is back propagation method. There have been many studies that have been conducted to apply artificial neural networks in stock market predictions. However, most stock market predictions only focus on US, Europeans and some Asian markets. To our knowledge, there are very few studies in stock market prediction for Saudi market. We tried to explore artificial neural networks using back-propagation algorithm to predict the Saudi market movement. We used the real datasets from the Saudi Stock Exchange (i.e., TADAWUL stock market exchange) and oil historical prices to evaluate the effectiveness of the proposed neural network methods. The results shows the capability of neural networks in predicting the stock exchange movement in Saudi market. |
Page(s) |
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62-70 |
ISSN |
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0975-3397 (Online) 2229-5631 (Print) |
Source |
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Vol. 10, Issue. 2 |
PDF |
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Download |
DOI |
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10.21817/ijcse/2018/v10i2/181002024 |
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