发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用260万份期权合约比较机器学习方法检测Black-Scholes模型偏差的能力，发现保留领域结构的树集成方法优于抽象嵌入，且偏差反映市场结构而非模型伪影。
AI 中文摘要
我们将期权定价视为一个表征问题：机器学习能否利用260万份真实期权合约检测出相对于Black-Scholes模型的系统性偏差？我们比较了三种机制：学习得到的抽象嵌入（核主成分分析）、保留领域结构的方法（基于树的集成）以及神经网络验证。基于树的方法在性能上优于核降维方法21.5个百分点（93.8%对比72.3%），且领域专家特征（希腊字母、货币性）优于工程化特征。基于神经网络和基于Black-Scholes的偏差标签在99.9974%的情况下一致，表明偏差反映的是市场结构而非模型伪影。我们得出结论：在具有专家设计符号特征的领域中，保留结构优于学习抽象。我们不声称存在可利用的错误定价。
英文摘要
We treat options pricing as a representation problem: can machine learning detect systematic deviations from Black-Scholes using 2.6M real option contracts? We compare three regimes: learned abstract embeddings (Kernel PCA), preserved domain structure (tree-based ensembles), and neural network validation. Tree-based methods outperform kernel dimensionality reduction by 21.5 percentage points (93.8% vs 72.3%), and domain-expert features (Greeks, moneyness) outperform engineered features. NN-based and BS-based deviation labels agree 99.9974% of the time, suggesting deviations reflect market structure rather than model artifact. We conclude that in domains with expert-designed symbolic features, preserving structure beats learning abstractions. We make no claim of exploitable mispricings.
Comments9 pages, 3 figures, accepted to Machina Stanford Journal
