Hybrid Machine Learning for Stock Return Prediction: Integrating Technical and Fundamental Information in the S&P 100

This thesis investigates whether integrating technical market data with fundamental accounting variables improves the out-of-sample predictability of stock returns in the highly liquid S&P 100 universe. Using an expanding window approach from 2010 to 2024, the study compares the performance of regularized linear (Ridge Regression) and non-linear (Histogram Gradient Boosting) estimators against an autoregressive benchmark. The empirical results reveal a divergence between statistical accuracy and economic value. Statistical tests reject the hypothesis that a hybrid feature set is necessary for minimizing forecast error; the Ridge Fundamental model achieved the lowest out-of-sample RMSE (0.0668), significantly outperforming the benchmark (p