用量子+经典模型融合，把股市方向预测准确率提到60.14%。
Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction
- 混合量子情感分析与决策变换器，多模型集成提升预测能力。
- 在标普500上达60.14%准确率，比单模型高3.10个百分点。
- 适合关注量化金融、机器学习应用的从业者和研究者。
金融市场预测是机器学习的难点任务，微小的方向准确性提升即具重大价值。现有模型普遍难以突破55%-57%准确率，受限于高噪声、非平稳性和市场有效性。本文提出一种混合集成框架，结合量子情感分析、决策变换器架构与策略性模型选择，在标普500方向预测上达到60.14%准确率，较单模型提升3.10%。该框架解决三大问题：其一，模型架构多样性优于数据集多样性——在相同数据上融合不同算法（LSTM、决策变换器、XGBoost、随机森林、逻辑回归）表现优于在多个数据集上训练同架构模型（60.14% vs. 52.80%），相关性分析显示同架构模型间相关系数r>0.6；其二，采用4量子比特变分量子电路增强情感分析，使各模型性能提升0.8%-1.5%；其三，通过智能过滤剔除弱预测器（准确率<52%），仅保留前7个强模型，使集成准确率达60.14%（全35模型为51.2%）。实验基于2020–2023年七种金融工具数据，覆盖疫情暴跌与通胀调整等多种市场状态。McNemar检验确认结果显著（p<0.05）。初步回测采用置信度筛选（6个以上模型共识）得夏普比1.2，优于买入持有策略的0.8，体现实际交易潜力。
原文摘要 · Abstract (English)
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.
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