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An Information-Theoretic Asset Pricing Model

Anisha Ghosh et al.

Journal of Financial Econometrics2025https://doi.org/10.1093/jjfinec/nbae033article
AJG 3ABDC A*
Weight
0.40

Abstract

We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading multi-factor models. The information stochastic discount factor (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (20–37%) and Sharpe ratio (1.1). The I-SDF extracted from a wide cross-section of equity portfolios is highly positively skewed and leptokurtic, and implies that about a third of the observed risk premia represent compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.

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https://doi.org/https://doi.org/10.1093/jjfinec/nbae033

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@article{anisha2025,
  title        = {{An Information-Theoretic Asset Pricing Model}},
  author       = {Anisha Ghosh et al.},
  journal      = {Journal of Financial Econometrics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/jjfinec/nbae033},
}

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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