Abstract
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.
Highlights
Can time series analysis be utilised for estimating stock trends? The short answer is yes
This study evaluates the application of the recurrent neural network to predict the stock market by employing real-time and experimental data pertaining to the Saudi stock market
An optimised artificial neural network (ANN) model is employed in this study that contributes to the prediction of the trend pertaining to the day‟s price for the Japanese stock market index
Summary
Can time series analysis be utilised for estimating stock trends? The short answer is yes. The optional changes pertaining to the observations that could not be described by the model Each of these time series is associated with an observation or a level. Most of these include noise or residual, while the trend and seasonality are optional. In a time series to determine the stock closing price each day, a key role is played by the stock exchanges for the development of main economic sectors. These have a considerable impact on people and nations worldwide. This study evaluates the application of the recurrent neural network to predict the stock market by employing real-time and experimental data pertaining to the Saudi stock market.
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