Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Olatunji, Sunday O.; Al-Ahmadi, Saad; Elshafei, Moustafa; Fallatah, Yaser Ahmad

doi:10.11648/j.ijiis.20130205.12

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…Meanwhile, to predict the Saudi stock prices, Olatunji et al [10] proposed an artificial neural network (ANN) model, applying on three major stock indices: Alrajhi bank, Saudi Telecom Company, and Saudi Basic Industries Corporation SABIC stocks. They only used the previous-day closing price as the model input.…”

Section: Literature Reviewmentioning

confidence: 99%

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Alturki¹,

Aldughaiyem²

2020

IJACSA

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This study tests the Saudi stock market weak form using the weak form of an efficient market hypothesis and proposes a recurrent neural network (RNN) to produce a trading signal. To predict the next-day trading signal of several shares in the Saudi stock market, we designed the RNN with a long shortterm memory architecture. The network input comprises several time series features that contribute to the classification process. The proposed RNN output is fed to a trading agent that buys or sells shares based on the share current value, current available balance, and the current number of shares owned. To evaluate the proposed neural network, we used the historical oil price data of Brent crude oil in combination with other stock features (e.g., previous day ( opening and closing price of the evaluated share). The results indicate that oil price variations affect the Saudi stock market. Furthermore, with 55% accuracy, the proposed RNN model produces the next-day trading signal. For the same period, the proposed RNN trading method achieves an investment gain of 23%, whereas the buy-and-hold method obtained 1.2%.

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Section: Literature Reviewmentioning

confidence: 99%

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Alturki¹,

Aldughaiyem²

2020

IJACSA

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“…It can also be considered as an indication of realizing share growth potential [1], [6], [17]. [6], [12][13], [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The main idea behind using these indicators is to evaluate stock price movements based on historical price patterns and volumes.…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.

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“…Next [6], [12][13], [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The main idea behind using these indicators is to evaluate stock price movements based on historical price patterns and volumes.…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur

2016

IJECE

View full text Add to dashboard Cite

<p>Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.</p>

show abstract

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Cited by 17 publications

References 21 publications

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

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