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

S. O. Olatunji; Mohammad Saad Al-Ahmadi; Moustafa Elshafei; Yaser Ahmed Fallatah

doi:doi:10.11648/j.ijiis.20130205.12

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

S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah

Published in International Journal of Intelligent Information Systems (Volume 2, Issue 5)

Received: 4 August 2013 Accepted: Published: 20 October 2013

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Abstract

In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence ‎in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.

Published in	International Journal of Intelligent Information Systems (Volume 2, Issue 5)
DOI	10.11648/j.ijiis.20130205.12
Page(s)	77-86
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Stock Markets, Stock Prices, Prediction Models, Forecasting, Artificial Neural Networks, Saudi Arabia

References

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APA Style

S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah. (2013). Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. International Journal of Intelligent Information Systems, 2(5), 77-86. https://doi.org/10.11648/j.ijiis.20130205.12

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ACS Style

S. O. Olatunji; Mohammad Saad Al-Ahmadi; Moustafa Elshafei; Yaser Ahmed Fallatah. Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. Int. J. Intell. Inf. Syst. 2013, 2(5), 77-86. doi: 10.11648/j.ijiis.20130205.12

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AMA Style

S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah. Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. Int J Intell Inf Syst. 2013;2(5):77-86. doi: 10.11648/j.ijiis.20130205.12

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@article{10.11648/j.ijiis.20130205.12,
  author = {S. O. Olatunji and Mohammad Saad Al-Ahmadi and Moustafa Elshafei and Yaser Ahmed Fallatah},
  title = {Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {2},
  number = {5},
  pages = {77-86},
  doi = {10.11648/j.ijiis.20130205.12},
  url = {https://doi.org/10.11648/j.ijiis.20130205.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20130205.12},
  abstract = {In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence ‎in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.},
 year = {2013}
}

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TY  - JOUR
T1  - Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model
AU  - S. O. Olatunji
AU  - Mohammad Saad Al-Ahmadi
AU  - Moustafa Elshafei
AU  - Yaser Ahmed Fallatah
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130205.12
DO  - 10.11648/j.ijiis.20130205.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 77
EP  - 86
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130205.12
AB  - In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence ‎in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.
VL  - 2
IS  - 5
ER  -

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Author Information

S. O. Olatunji

Computer Science Department, Adekunle Ajasin University, Akungba Akoko, Nigeria
Mohammad Saad Al-Ahmadi

King Fahd Univ. of Pet. & Mineral, (KFUPM), Dhahran, Saudi Arabia
Moustafa Elshafei

King Fahd Univ. of Pet. & Mineral, (KFUPM), Dhahran, Saudi Arabia
Yaser Ahmed Fallatah

King Fahd Univ. of Pet. & Mineral, (KFUPM), Dhahran, Saudi Arabia

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Plain Text BibTeX RIS

APA Style

S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah. (2013). Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. International Journal of Intelligent Information Systems, 2(5), 77-86. https://doi.org/10.11648/j.ijiis.20130205.12

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ACS Style

S. O. Olatunji; Mohammad Saad Al-Ahmadi; Moustafa Elshafei; Yaser Ahmed Fallatah. Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. Int. J. Intell. Inf. Syst. 2013, 2(5), 77-86. doi: 10.11648/j.ijiis.20130205.12

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AMA Style

S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah. Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model. Int J Intell Inf Syst. 2013;2(5):77-86. doi: 10.11648/j.ijiis.20130205.12

Copy | Download

@article{10.11648/j.ijiis.20130205.12,
  author = {S. O. Olatunji and Mohammad Saad Al-Ahmadi and Moustafa Elshafei and Yaser Ahmed Fallatah},
  title = {Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {2},
  number = {5},
  pages = {77-86},
  doi = {10.11648/j.ijiis.20130205.12},
  url = {https://doi.org/10.11648/j.ijiis.20130205.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20130205.12},
  abstract = {In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence ‎in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model
AU  - S. O. Olatunji
AU  - Mohammad Saad Al-Ahmadi
AU  - Moustafa Elshafei
AU  - Yaser Ahmed Fallatah
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijiis.20130205.12
DO  - 10.11648/j.ijiis.20130205.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 77
EP  - 86
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20130205.12
AB  - In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence ‎in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.
VL  - 2
IS  - 5
ER  -

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