{"slug": "learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black", "title": "Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes", "summary": "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.", "body_md": "arXiv:2609.27764v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black", "canonical_source": "https://www.machinebrief.com/news/learning-to-detect-symbolic-failure-machine-learning-and-the-8glu", "published_at": "2026-09-24 04:00:00+00:00", "updated_at": "2026-09-24 05:00:09.658341+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks"], "entities": ["Black-Scholes", "Kernel PCA", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black", "markdown": "https://wpnews.pro/news/learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black.md", "text": "https://wpnews.pro/news/learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black.txt", "jsonld": "https://wpnews.pro/news/learning-to-detect-symbolic-failure-machine-learning-and-the-limits-of-black.jsonld"}}