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.