Universiti Teknologi Malaysia Institutional Repository

A recurrent neural network and a discrete wavelet transform to predict the Saudi stock price trends

Jarrah, M. and Salim, N. (2019) A recurrent neural network and a discrete wavelet transform to predict the Saudi stock price trends. International Journal of Advanced Computer Science and Applications, 10 (4). ISSN 2158-107X


Official URL: http://dx.doi.org/10.14569/ijacsa.2019.0100418


Stock markets can be characterised as being complex, dynamic and chaotic environments, making the prediction of stock prices very tough. In this research work, we attempt to predict the Saudi stock price trends with regards to its earlier price history by combining a discrete wavelet transform (DWT) and a recurrent neural network (RNN). The DWT technique helped to remove the noises pertaining to the data gathered from the Saudi stock market based on a few chosen samples of companies. Then, a designed RNN has trained via the Back Propagation Through Time (BPTT) method to aid in predicting the Saudi market's stock prices for the next seven days' closing price pertaining to the chosen sample of companies. Then, analysis of the obtained results was carried out to make a comparison with the results from those employing the traditional prediction algorithms like the auto regressive integrated moving average (ARIMA). Based on the comparison, it was found that the put forward method (DWT+RNN) allowed more accurate prediction of the day's closing price versus the ARIMA method employing the mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) criterion.

Item Type:Article
Uncontrolled Keywords:deep learning, discrete wavelet transform (DWT), prediction
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:90185
Deposited By: Narimah Nawil
Deposited On:30 Mar 2021 15:48
Last Modified:30 Mar 2021 15:48

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