What question did this study set out to answer?

This research aims to resolve the equity premium puzzle by introducing multiple dimensions in analysis. It challenges existing models by incorporating newer verification techniques.

July 7, 2026Open Access

Resolving the Equity Premium Requires at Least Two Dimensions: A Machine-Checked Class No-Go and a Term-Structure Discriminator

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This research aims to resolve the equity premium puzzle by introducing multiple dimensions in analysis. It challenges existing models by incorporating newer verification techniques.
Utilizes a machine-checked class for formal verification with Lean 4 and Python scripts.
Calibrates consumption-based asset pricing models to assess risk aversion and consumption smoothness.
Examines equity premium prediction in the context of term-structure discrimination.
Demonstrates that standard asset pricing models inadequately predict the equity premium under traditional measures.
Reveals that incorporating more dimensions can significantly alter predictions and conclusions regarding equity premium.
Highlights the necessity for formal verification in asset pricing models to enhance reliability and accuracy.

Implication

Randomized trial investigates equity premium using multidimensional approaches, suggesting new verification methods.

Abstract

The equity premium puzzle of Mehra and Prescott (1985) is the observation that the standard consumption-based asset pricing model, calibrated to plausible risk aversion and the observed smoothness of aggregate consumption, predicts an equity premium Maturity: Draft. Target venue: Journal of Economic Theory (primary); Management Science (backup). Includes formal verification (Lean 4 with Python verification scripts). Part of The Latent research program.

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Cite This Study

Tamás Nagy (2026) studied this question.

synapsesocial.com/papers/6a4c96be331bc25c9e5f40d0 https://doi.org/10.5281/zenodo.21205597

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Implication

Randomized trial investigates equity premium using multidimensional approaches, suggesting new verification methods.

Resolving the Equity Premium Requires at Least Two Dimensions: A Machine-Checked Class No-Go and a Term-Structure Discriminator

Key Points

Implication

Abstract

Discussion

Cite This Study

Resolving the Equity Premium Requires at Least Two Dimensions: A Machine-Checked Class No-Go and a Term-Structure Discriminator

Key Points

Implication

Abstract

Discussion

Cite This Study

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