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

TL;DR

This work tackles the persistent challenge of directional accuracy in SP500 forecasting by proposing a hybrid quantum-classical ensemble that combines architecture diversity (LSTM, Decision Transformer, XGBoost, Random Forest, Logistic Regression), a 4-qubit variational quantum sentiment module, and smart model filtering. The method achieves 60.14% directional accuracy on 286 out-of-sample predictions, with a statistically significant 3.10% gain over the best single model, driven by error decorrelation and complementary inductive biases. Quantum features provide modest but consistent gains, particularly for volatility-focused predictions, while the Top-7 architecture-diverse ensemble and quality filtering are crucial to the performance boost. The results suggest near-term practical viability, offering a production-friendly approach with fast training and inference, though profitability depends on accounting for costs and regime dynamics; future work should broaden data modalities and validate on real quantum hardware and across markets.

Abstract

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.

Figures (4)

• Figure 1: Top 15 model performance across dataset-architecture combinations. Green bars indicate models exceeding 55% accuracy threshold. VIX-based models (LSTM, Decision Transformer) achieve highest accuracy (57.04%, 56.99%), followed by small-cap Russell 2000 models. Technology sector (XLK) and corporate bonds (HYG) consistently underperform, validating smart filtering approach. The Top-7 ensemble selects models above the dashed red line (52% threshold).
• Figure 2: Dataset × Model performance heatmap showing accuracy across all 35 combinations (7 datasets × 5 architectures). Darker green indicates higher accuracy. VIX-based models excel across all architectures (top row), while XLK struggles universally (middle row). Decision Transformer shows competitive performance with LSTM on volatility data but fails on low-signal regimes (HYG, XLF). This visualization guided our smart filtering approach—only green cells (>52%) contribute to final ensemble.
• Figure 3: Improvement analysis showing accuracy gains/losses versus best individual model (VIX_LSTM: 57.04%, dashed line). Green bars indicate strategies exceeding best individual; red bars show degradation. Top-7 selection achieves largest gain (+3.10%), while Dataset-LSTM suffers largest loss (-4.24%). The stark contrast between Top-7 (architecture diversity) and Dataset-LSTM (dataset diversity) empirically demonstrates our core finding: combining different learning algorithms matters more than combining different data sources.
• Figure 4: Prediction correlation matrix among 9 selected high-quality models. Average pairwise correlation: 0.42—high enough to benefit from aggregation, low enough to avoid redundancy. Key finding: models sharing same architecture exhibit higher correlation (VIX_LSTM vs SP500_LSTM: $r = 0.61$, yellow cells) than different architectures on same data (VIX_LSTM vs VIX_DecisionTransformer: $r = 0.38$, blue cells). This empirically validates our framework's emphasis on architecture diversity over dataset diversity.