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

ArXiv ID: 2512.15738 “View on arXiv”

Authors: Abraham Itzhak Weinberg

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.

Keywords: Ensemble Learning, Decision Transformer, Quantum Sentiment Analysis, Market Prediction, Statistical Arbitrage, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.2/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics like variational quantum circuits and correlation analysis, while providing concrete backtesting results (Sharpe ratio 1.2) and statistical tests (McNemar’s p<0.05) on real market data.

  flowchart TD
    A["Research Goal: >60% Directional Accuracy"] --> B["Hybrid Ensemble Framework"]
    B --> C{"Key Components"}
    C --> D["Architecture Diversity"]
    C --> E["Quantum Sentiment Analysis"]
    C --> F["Smart Filtering"]
    D --> G["4 Models: LSTM, DT, XGBoost, RF"]
    E --> H["4-Qubit Circuit"]
    F --> I["Top-7 Predictors"]
    G & H & I --> J["Ensemble Learning"]
    J --> K["Outcome: 60.14% Accuracy"]
    K --> L["Sharpe Ratio: 1.2 vs 0.8"]