Large and Deep Factor Models

Bryan Kelly, Boris Kuznetsov, Semyon Malamud and Yuan Zhang

Papers from arXiv.org

Abstract: We show that a deep neural network (DNN) trained to construct a stochastic discount factor (SDF) admits an additive decomposition separating nonlinear characteristic discovery from the pricing rule that aggregates them. This decomposition yields a linear factor representation governed by the Portfolio Tangent Kernel (PTK), which summarizes the network's learned features. In population, the implied SDF converges to a ridge-regularized version of the true SDF, with the degree of regularization determined by spectral complexity. Empirically, using U.S. equity data, the PTK representation delivers economically and statistically significant performance gains, while rising spectral complexity imposes tighter limits on finite-sample pricing.

Date: 2024-01, Revised 2026-06
New Economics Papers: this item is included in nep-big and nep-cmp
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Related works:
Working Paper: Large and Deep Factor Models (2026)
Working Paper: Large (and Deep) Factor Models (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2402.06635

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