用于算法交易的表格型深度学习：面向股票信号生成的跨制度贝叶斯优化

中文摘要 AI 辅助

算法交易当前代表着一个规模超过200亿美元的市场，在该市场中，信号鲁棒性的哪怕是微小增益都能转化为具有经济意义的收益。现有股票预测模型的评估并未在超参数选择过程中明确针对制度鲁棒性。本研究针对约300只美国大盘股11年的每日观测数据训练了五类模型，并配置贝叶斯优化以覆盖三种统计上不同的市场制度下的交易表现。制度鲁棒的超参数选择与样本外泛化能力相关，因为在测试期的四个季度中，信号精度始终高于随机基准，且投资组合表现会在模拟输入噪声下缓慢下降，超过特定阈值后才会崩溃。没有任何单一表格型深度学习架构的表现优于梯度提升树，但通过秩聚合结合XGBoost与TabNet的混合集成模型实现了51.26%的年化收益率、2.44的夏普比率以及0.423的统计显著CAPM alpha（p=0.011）。接近零的beta表明，这种超额表现源于选股而非市场敞口。在考虑技术和基本面特征后，替代数据仅起次要作用，且其在空头端的贡献强于多头端，贡献程度随模型类别变化。一款交互式应用可实时呈现这些结果，剩余的实时数据整合步骤将推动其实际部署。

英文摘要

Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns. Existing evaluations of equity prediction models do not explicitly target regime robustness during hyperparameter selection. Five model classes are trained on daily observations from approximately 300 large-cap US equities over eleven years, with Bayesian optimisation configured to target trading performance across three statistically different market regimes. Regime-robust hyperparameter selection is associated with out-of-sample generalisation, as signal precision remains above the random baseline across all four quarters of the test period, and portfolio performance slowly degrades under simulated input noise before collapsing beyond a defined threshold. No individual tabular deep learning architecture outperforms gradient-boosted trees, but combining XGBoost and TabNet using rank aggregation produces a Hybrid ensemble with an annualised return of 51.26%, a Sharpe ratio of 2.44, and a statistically significant CAPM alpha of 0.423 (p = 0.011). A near-zero beta indicates this outperformance is driven by stock selection, not market exposure. Alternative data plays a secondary role once technical and fundamental features are accounted for, as well as contributing more strongly on the short side than the long, and varies by model class. An interactive application makes these results explorable in real time, with live data integration the remaining step toward practical deployment.

发表机构

• University of Exeter（埃克塞特大学）

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