Volume 2, Issue 5, October 2013, Page: 77-86
Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model
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
Received: Aug. 4, 2013;       Published: Oct. 20, 2013
DOI: 10.11648/j.ijiis.20130205.12      View  3651      Downloads  231
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.
Stock Markets, Stock Prices, Prediction Models, Forecasting, Artificial Neural Networks, Saudi Arabia
To cite this article
S. O. Olatunji, Mohammad Saad Al-Ahmadi, Moustafa Elshafei, Yaser Ahmed Fallatah, Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model, International Journal of Intelligent Information Systems. Vol. 2, No. 5, 2013, pp. 77-86. doi: 10.11648/j.ijiis.20130205.12
Abdolreza, M., P. Fahime, et al. (2009). "A New Approach Based on Artificial Neural Networks for Prediction of High Pressure Vapor-liquid Equilibrium." Australian Journal of Basic and Applied Sciences 3(3): 1851-1862.
Ali, J. K. (1994). Neural Networks: A New Tool for the Petroleum Industry. European Petroleum Computer Conference, Aberdeen, U.K.
Atsalakis, G. S. and K. P. Valavanis (2009). "Surveying stock market forecasting techniques - Part II: Soft computing methods." Expert Systems with Applications 36(3, Part 2): 5932-5941.
Bajestani, N. S. and A. Zare (2011). "Forecasting TAIEX using improved type 2 fuzzy time series." Expert Systems with Applications 38(5): 5816-5821.
Boyacioglu, M. A. and D. Avci (2010). "An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange." Expert Systems with Applications 37(12): 7908-7912.
Castillo, E., B. Guijarro-Berdi ˜nas, et al. (2006). "A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis." Journal of Machine Learning Research 7: 1159-1182.
Castillo, O., O. Fontenla-Romero, et al. (2002). "A global optimum approach for one-layer neural networks." Neural Computation 14(6): 1429-1449.
Chang, P.-C., C.-H. Liu, et al. (2009). "A neural network with a case based dynamic window for stock trading prediction." Expert Systems with Applications 36(3, Part 2): 6889-6898.
Chih-Fong, T. and H. Yu-Chieh (2010). "Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches." Decis. Support Syst. 50(1): 258-269.
Duda, R. O., P. E. Hart, et al. (2001). Pattern Classification. New York, John Wiley and Sons.
Esfahanipour, A. and W. Aghamiri (2010). "Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis." Expert Systems with Applications 37(7): 4742-4748.
Guresen, E., G. kayakutlu, et al. "Using Artificial Neural Network Models in Stock Market Index Prediction." Expert Systems with Applications In Press, Accepted Manuscript.
Hadavandi, E., H. Shavandi, et al. (2010). "Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting." Knowledge-Based Systems 23(8): 800-808.
Kara, Y., M. Acar Boyacioglu, et al. (2010). "Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange." Expert Systems with Applications In Press, Corrected Proof.
Lean Yu, Huanhuan Chen, et al. (2009). "Evolving Least Squares Support Vector Machines for Stock Market Trend Mining." IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION VOL. 13(NO. 1).
Lee, M.-C. (2009). "Using support vector machine with a hybrid feature selection method to the stock trend prediction." Expert Systems with Applications 36(8): 10896-10904.
Liang, Q. and J. M. Mendel (2000). "Equalization of Non-linear Time-Varying Channels Using Type-2 Fuzzy Adaptive Filters." IEEE Trans. on Fuzzy Systems 8: 551-563.
Mabu, S., Y. Chen, et al. (2009). "Stock price prediction using neural networks with RasID-GA." IEEJ Transactions on Electrical and Electronic Engineering 4(3): 392-403.
Md. Rafiul, H. (2009). "A combination of hidden Markov model and fuzzy model for stock market forecasting." Neurocomput. 72(16-18): 3439-3446.
Meysam, A., G. Mohsen, et al. (2011). "Design and analysis of experiments in ANFIS modeling for stock price prediction " International Journal of Industrial Engineering Computations 2: 409-418.
Ni, L.-P., Z.-W. Ni, et al. (2011). "Stock trend prediction based on fractal feature selection and support vector machine." Expert Systems with Applications 38(5): 5569-5576.
Shen, W., X. Guo, et al. (2010). "Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm." Knowledge-Based Systems In Press, Corrected Proof.
Tutu, H., E. M. Cukrowska, et al. (2005). "Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield, South Africa." Environmental Modeling and Assessment 10: 143-152.
Browse journals by subject