{"ID":2832329,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15738","arxiv_id":"2512.15738","title":"Hybrid Quantum-Classical Ensemble Learning for S\\\u0026P 500 Directional Prediction","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\\\u0026P 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 ($r\u003e0.6$ 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 $\u003c52\\%$), 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 ($p\u003c0.05$). 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.","short_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...","url_abs":"https://arxiv.org/abs/2512.15738","url_pdf":"https://arxiv.org/pdf/2512.15738v1","authors":"[\"Abraham Itzhak Weinberg\"]","published":"2025-12-06T22:22:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"q-fin.ST\"]","methods":"[\"Transformer\"]","has_code":false}