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

TL;DR

This paper presents a hybrid quantum-classical ensemble learning framework that improves S ext–P 500 directional prediction accuracy to over 60%, leveraging diverse models, quantum sentiment analysis, and strategic filtering for practical trading benefits.

Contribution

It introduces a novel hybrid ensemble approach combining quantum and classical models, emphasizing architecture diversity and strategic filtering to outperform prior methods.

Findings

Achieved 60.14% accuracy on S ext–P 500 prediction.

Quantum sentiment analysis provides +0.8% to +1.5% gains.

Filtering weak predictors improves ensemble performance.

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.