Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction

Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55--57\% accuracy due to high noise, non-stationarity, and market efficiency. We introduce a hybrid ensemble framework combining quantum sentiment analysis, Decision Transformer architecture, and strategic model selection, achieving 60. 14\% directional accuracy on S\&P 500 prediction, a 3. 10\% improvement over individual models. Our framework addresses three limitations of prior approaches. First, architecture diversity dominates dataset diversity: combining different learning algorithms (LSTM, Decision Transformer, XGBoost, Random Forest, Logistic Regression) on the same data outperforms training identical architectures on multiple datasets (60. 14\% vs. \ 52. 80\%), confirmed by correlation analysis (r>0. 6 among same-architecture models). Second, a 4-qubit variational quantum circuit enhances sentiment analysis, providing +0. 8\% to +1. 5\% gains per model. Third, smart filtering excludes weak predictors (accuracy <52\%), improving ensemble performance (Top-7 models: 60. 14\% vs. \ all 35 models: 51. 2\%). We evaluate on 2020--2023 market data across seven instruments, covering diverse regimes including the COVID-19 crash and inflation-driven correction. McNemar's test confirms statistical significance (p<0. 05). Preliminary backtesting with confidence-based filtering (6+ model consensus) yields a Sharpe ratio of 1. 2 versus buy-and-hold's 0. 8, demonstrating practical trading potential.