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  • 标题:Saudi Arabia Stock Market Prediction Using Neural Network
  • 作者:Talal Alotaibi ; Amril Nazir ; Roobaea Alroobaea
  • 期刊名称:International Journal on Computer Science and Engineering
  • 印刷版ISSN:2229-5631
  • 电子版ISSN:0975-3397
  • 出版年度:2018
  • 卷号:10
  • 期号:2
  • 页码:62-70
  • 出版社:Engg Journals Publications
  • 摘要: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.
  • 关键词:stock market prediction; Saudi Arabia stock prediction; neural network stock prediction.
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