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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
