(untitled) (Global Journal of Sciences, 2025)
This research aims to improve forecasting models for the volatility of stock prices in Saudi banks by using machine learning algorithms and parameter optimization techniques such as Grid Search. The goal of this study is to improve forecasting models. Grid Search is going to be used in order to attain this goal. We examined the price information that Al Rajhi Bank has accumulated over a considerable length of time and the results were analyzed. The starting and closing prices, the highest and lowest values, the trading volume, and the daily percentage change were all included in this. Additionally, the trade volume was an important factor. In order to throw light on the general patterns that were present, we used descriptive data analysis. Given that the Saudi financial market is driven by both internal and external variables, the data indicated that there was a substantial degree of variation in both the trading volume and the price. This is typical for the Saudi market since it is influenced by both types of factors. Next, a machine learning model that was produced based on Random Forest was developed, along with a linear regression model and a ridge model. After that these models were constructed. Taking into account the outcomes of the research, the Random Forest model, which had been enhanced via the use of Grid Search, was the most accurate in terms of predicting future volatility. The coefficient of determination (R2) value of about 0.86 makes it superior than linear models in terms of its capacity to make assumptions and predictions. Moreover, the results indicate the need of using nonlinear models in combination with optimization methodologies in order to focus on the complicated relationships that exist between financial issues and price fluctuations. This is because the outcomes highlight the need to use nonlinear models.