量子卷积网络提升股票收益预测，实测表现优于经典模型72%。
Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
- 融合时序编码与量子卷积，利用量子叠加与纠缠增强特征表达。
- 在东京证券交易所数据上实现0.538的夏普比率，比最优经典模型高72%。
- 适合量化金融领域研究者，尤其关注量子计算在投资决策中的应用。
量子机器学习为复杂、嘈杂且高度动态的金融市场预测提供了新路径。然而，许多经典预测模型在噪声输入、市场结构突变和泛化能力方面存在局限。为此，我们提出量子时序卷积神经网络（QTCNN），结合经典时序编码器与参数高效的量子卷积电路，用于横截面股票收益预测。时序编码器从技术指标序列中提取多尺度模式，而量子处理则利用量子叠加与纠缠机制增强特征表示并抑制过拟合。我们在JPX东京证券交易所数据集上开展全面基准测试，通过构建多空投资组合，以样本外夏普比率为主要评估指标。QTCNN取得0.538的夏普比率，优于最佳经典基线约72%。结果表明，量子增强预测模型在量化金融中具有实际应用潜力。
原文摘要 · Abstract (English)
Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shifts, and limited generalization capacity. To address these challenges, we propose a Quantum Temporal Convolutional Neural Network (QTCNN) that combines a classical temporal encoder with parameter-efficient quantum convolution circuits for cross-sectional equity return prediction. The temporal encoder extracts multi-scale patterns from sequential technical indicators, while the quantum processing leverages superposition and entanglement to enhance feature representation and suppress overfitting. We conduct a comprehensive benchmarking study on the JPX Tokyo Stock Exchange dataset and evaluate predictions through long-short portfolio construction using out-of-sample Sharpe ratio as the primary performance metric. QTCNN achieves a Sharpe ratio of 0.538, outperforming the best classical baseline by approximately 72\%. These results highlight the practical potential of quantum-enhanced forecasting model, QTCNN, for robust decision-making in quantitative finance.
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