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Abstract

Predicting the stock market via data analysis is an important research topic. Because the system implementing it seems to think and act like humans, for the last two decades’ machine learning got a lot of attention from developers, programmers, scientists, even from the general public. Due to their advantages, machine learning techniques can be used in many areas, including finance and stock markets. Although many studies were performed with excellent results for the investors, many researchers are still looking for better results that will help minimize risk with higher percentage. In this study, the state of the techniques to predict stock markets will be used and applied in the Saudi market. Then all the results will be compared with deep learning approaches. There are many new algorithms developed, and when it comes to prediction, their accuracy is high. Nowadays, the companies gather large amount of data that can be used to test these new algorithms. However, the current literature does not have many examples with 100 % accuracy in predicting the stock market, especially for the Saudi stock market. Nowadays stock markets became a very strong factor that affects the country’s economy and motivate investors. This research focuses on a stock prediction system that 1) predicts the market by classifications algorithms, 2) predicts the market by regression algorithms, and 3) predicts the market by a deep learning algorithm. The objective is to determine which approach works best and predicts the price as close as possible to the actual ones.

Keywords

Classification, Deep learning, Machine learning, Majority voting, Regression, Saudi stock market

Article Type

Article

First Page

51

Last Page

55

Publication Date

12-31-2025

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