Forecasting of Saudi Stock Prices Using Statistical and Machine Learning Models: A Multi-Model Comparative Approach

Accurate forecasting of financial time-series data is not just a challenge—it's a critical necessity for investors in emerging markets. This study decisively evaluates the predictive power of seven advanced statistical and machine learning models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest, eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Decision Tree, across eight major stocks on the Saudi Stock Exchange (Tadawul). Employing a robust, lag-based forecasting framework, we meticulously assessed model performance using RMSE, R², directional accuracy, and computational efficiency. We introduce a hybrid evaluation framework that integrates magnitude accuracy, directional precision, and runtime profiling to guide model selection at the individual stock level, an approach that has not been previously applied to the Saudi market. The empirical evidence is compelling: model selection is heavily stock-specific. The classical ARIMA model consistently outperformed the others, delivering the lowest error and highest goodness-of-fit for stable, high-capitalization stocks, underscoring the timeless relevance of linear autoregressive components. Conversely, the ensemble method XGBoost emerged as a powerhouse of computational efficiency and predictive balance for more volatile series, boasting an optimal operational profile (runtime of ~ 1.5 s). While deep learning (LSTM) and SVR models fall short of magnitude metrics owing to the low signal-to-noise ratio in daily close price data, these findings offer practical guidance for investors, analysts, and policymakers seeking scalable, stock-specific forecasting strategies. Considering Saudi Arabia’s Vision 2030 and the increasing demand for real-time financial intelligence, this research addresses the urgent need for scalable stock-specific forecasting frameworks that support investor decision-making and policy formulation.