Improving Stock Price Volatility Prediction Models Using Machine Learning and Statistical Techniques (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.
Keywords: Stock price volatility; Machine learning; Grid search; Financial prediction; Random forest; Linear regression