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