Machine Learning-Based Stock Return Prediction: Evidence from the Saudi Arabian Stock Market (Tadawul)

Abstract

Return predictability on the Saudi Arabian Stock Exchange (Tadawul), the largest equity market in the Middle East, remains underexplored relative to its structural distinctiveness as an oil-linked, retail-dominated emerging market. We compare 11 predictive models spanning five linear (regularised) regressors, three tree-based ensembles, and three stacked hybrid architectures. This comparison quantifies the improvement that nonlinear and ensemble methods offer over linear benchmarks for daily return prediction in this setting and identifies which method delivers the best accuracy-versus-cost trade-off for practical deployment. Using 28,750 daily observations (January 2015–December 2025), we constructed a 40-feature technical signal space spanning six families and evaluated all 11 models under a strict chronological train–validate–test protocol with an 18-month sealed holdout. A Lasso–XGBoost stacked ensemble achieves the lowest test RMSE of 0.906 and an out-of-sample Information Coefficient of 0.133, outperforming linear benchmarks by 15–27% in forecast error. Translated into a long-short strategy subject to 0.6% round-trip transaction costs, the optimal model delivers a Sharpe ratio of 0.587, an 81.1% win rate and a maximum drawdown of −5.53% across 758 trades. Sensitivity analysis confirms robustness across hyperparameter grids and rolling estimation windows. Bollinger Band Width, cross-sectional stock identity and lagged MACD signals collectively dominate feature importance rankings.