Abstract: This study tests the Saudi stock market weak form using the weak form of an efficient market hypothesis and proposes a recurrent neural network (RNN) to produce a trading signal. To predict the next-day trading signal of several shares in the Saudi stock market, we designed the RNN with a long short-term memory architecture. The network input comprises several time series features that contribute to the classification process. The proposed RNN output is fed to a trading agent that buys or sells shares based on the share current value, current available balance, and the current number of shares owned. To evaluate the proposed neural network, we used the historical oil price data of Brent crude oil in combination with other stock features (e.g., previous day (opening and closing price of the evaluated share). The results indicate that oil price variations affect the Saudi stock market. Furthermore, with 55% accuracy, the proposed RNN model produces the next-day trading signal. For the same period, the proposed RNN trading method achieves an investment gain of 23%, whereas the buy-and-hold method obtained 1.2%.

Fahd A. Alturki and Abdullah M. Aldughaiyem, “Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110963

@article{Alturki2020,
title = {Trading Saudi Stock Market Shares using Multivariate Recurrent Neural Network with a Long Short-term Memory Layer},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110963},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110963},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Fahd A. Alturki and Abdullah M. Aldughaiyem}
}