اكادميون سعوديون يحددون طرق استخدام التحليل الفنى فى سوق الاسهم السعودى
 

Using Neural Network
Talal Alotaibi, Amril Nazir, Roobaea Alroobaea, Moteb Alotibi, Fasal Alsubeai, Abdullah Alghamdi,
Thamer Alsulimani
Department of Computer Science, College of Computers and Information Technology
Taif University, Taif, KSA
(تم حذف الإيميل لأن عرضه مخالف لشروط المنتدى), (تم حذف الإيميل لأن عرضه مخالف لشروط المنتدى)
Abstract—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., TA 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.
Keywords— stock market prediction; Saudi Arabia stock prediction; neural network stock prediction.
I. INTRODUCTION
Prediction of stock market is gaining a lot of attention because it can help financial organization and investors
to make better investment decisions. Many researchers in artificial intelligence community have proposed diverse
kind of technical indicators, fundamental analysis, and statistical techniques such as Regression, and genetic
algorithms. However, most of these techniques or the combination techniques were not very successful to predict
the stock market movement accurately [1]. However, artificial neural networks showed the opposite especially in
the prediction filed and it have become objects of everyday use and have shown to be very effective in many
fields such as signal filtering in computer modems, speech recognition and optical character recognition etc.
Moreover, neural networks have been currently applied in classification, intelligent control, medicine, pattern
analysis, function approximation, weather forecasting etc. This success has proved neural networks as an
adequate model and method in real life use cases. Many researchers have also conducted some studies on the
utilization of artificial neural networks in predicting the stock markets and they have shown that the approach can
provide powerful performance in stock market prediction due to their ability to model nonlinear functions, since
most stock markets is complex(nonlinear) and volatile[1, 2]. The powerful ability shown by neural networks are
due to: (1) its ability to find a relationships between input and output even if the problem is very complicated and
figure out exactly what it has learnt; (2) The new patterns can recognized even if they haven’t been in training set;
(3) Neural networks has the ability to approximate any function that enables us to learn an intricate relationships
between the system input and the output.
There are many research that been conducted in the area using artificial neural networks for stock market
predictions. However, to our knowledge, there is a very rare study on stock market prediction on Saudi market. In
this work we will examine and present the artificial network technique to predict the Saudi stock market
movement. We used real datasets from the TA stock market exchange to evaluate the effectiveness of
the proposed technique. Following section reviews the literature for existing methods.
II. LITERATURE REVIEW
The studies of forecasting the movement of the stock market have been growing in recent years. One of the
methods that have been used for predicting the stock market movements and prices is artificial neural network
(ANN).Yao, Jingtao, and Chew Lim Tan [3] explains that back-propagation network used in his study has given
good results for forecasting. Guresen et al [4] also did research in stock market index prediction using neural
networks. Chen et al [11] in their work on predicting the direction of index return for Taiwan stock exchange was
a promosing one.
Khoa et al, [11] used a systematic method to investigate if it can predict the stock market price using artificial
neural networks with back propagation. A lot of improvement appeared after the integration of the time factors
algorithm and profit when we compare it with the classic(traditional) training for Feed Forward Network.
Khashei et al [12], used artificial neural networks (ANNs) model for time series forecasting and got prediction
with a high degree of accuracy. Chan et al [13], investigate the neural networks to predict Financial Time Series.
The experimental results based on historical data it is possible to modeling stock price using three layer neural
e-ISSN : 0975-3397
p-ISSN : 2229-5631
Talal Alotaibi et al. / International Journal on Computer Science and Engineering (IJCSE) DOI: 10.21817/ijcse/2018/v10i2/181002024 Vol. 10 No.2 Feb 2018 62
network. Nordberg M and Karlsson S, used ANN for Stock market index prediction trained on foreign
markets[14].
Ye, Q and Wei, L. [15], used Wavelet Neural Network for Prediction of Stock Price and the simulation
results of Shanghai index data discovered that the improvement changes that is performed on WNN method is
effective and the model showed up a good prediction performance. Yao et al, conduct a study on the usage of
artificial neural network to perform technical predict of . It shows that back-propagation network used in
the present study [16] has proved to be adequate for forecasting. Guresen et al used the models of artificial neural
network in the prediction of stock market index and it shows that the results obtained in ANN was fairly accurate.
Leung and Hazem [4] used neural networks for forecasting and trading the Taiwan Stock Index and it shows that
the models of neural networks are very useful in forecasting the movement and direction of index return.
Kara et al [9], investigate on the capability of support vector machine and artificial neural networks on the
prediction of the direction of the stock price index movement. it showed that the accuracy of ANN model (75.7%)
is better than SVM model (71.5%). Grigoryan [10] and Ghezelbash [19] designed and implemented artificial
neural network System for stock exchange prediction and found that artificial neural network is useful prediction
tools for stock market.
Chen, W. H., Shih, J. Y., & Wu, conducted a research [6] on comparing between artificial neural networks
and support vector machines. it showed that both models perform good and it shows that both method had the
ability to predict the stock market. Ou, P., and Wang, H. conducted a research on using ten data mining
techniques to predicting stock market index movement.
In this work we will examine the applicability of ANN in forecasting the movement of Saudi stock market
and analyze the results.
III. NETWORK MODEL FOR STOCK MARKET PREDICTION