Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes

A study of 2.6 million real option contracts found tree-based ensemble models detected deviations from Black-Scholes pricing with 93.8% accuracy, beating Kernel PCA dimensionality reduction by 21.5 percentage points (72.3%), according to the arXiv paper 2609.27764v1. The authors also reported that neural-network-based and Black-Scholes-based deviation labels agreed 99.9974% of the time, which they said suggests the deviations reflect market structure rather than model artifact, and concluded that preserving expert-designed symbolic features such as Greeks and moneyness outperforms learning abstract embeddings in such domains. The paper makes no claim of exploitable mispricings.

arXiv:2609.27764v1 Announce Type: new 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.