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Beyond Black-Box AI: A Theory of Interpretable Transformers for Asset Pricing

https://doi.org/10.2139/ssrn.5161644
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Approximation by superpositions of a sigmoidal function
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On the measurement of inequality
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Common risk factors in the returns on stocks and bonds
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Multilayer feedforward networks are universal approximators
inferredcites →https://doi.org/10.1016/j.jeconom.2023.105574openalex-snapshot
Asset pricing with neural networks: Significance tests
inferredcites →https://doi.org/10.1016/j.neunet.2017.07.002openalex-snapshot
Error bounds for approximations with deep ReLU networks
inferredcites →https://doi.org/10.1093/rfs/hhaa009openalex-snapshot
Empirical Asset Pricing via Machine Learning
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Regularization and Variable Selection Via the Elastic Net
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A Test for the Number of Factors in an Approximate Factor Model
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Forest through the Trees: Building Cross‐Sections of Stock Returns
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On the distribution of the largest eigenvalue in principal components analysis
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Entropy and the Value of Information for Investors
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The Econometrics of Financial Markets
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Dissecting Characteristics Nonparametrically
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Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns
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Growing the Efficient Frontier on Panel Trees
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