Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes
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Computer Science > Machine Learning
Title:Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes
Abstract: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.
| Comments: | 9 pages, 3 figures, accepted to Machina Stanford Journal |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2609.27764 [cs.LG] |
| (or arXiv:2609.27764v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.27764
arXiv-issued DOI via DataCite (pending registration)
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