Working Paper: CEPR ID: DP15717
Authors: Pooya Molavi; Alireza Tahbazsalehi; Andrea Vedolin
Abstract: This paper analyzes how limits to the complexity of statistical models used by market participants can shape asset prices. We consider an economy in which agents can only entertain models with at most k factors, where k may be distinct from the true number of factors that drivethe economy’s fundamentals. We first characterize the implications of the resulting departure from rational expectations for return dynamics and relate the extent of return predictability at various horizons to the number of factors in the agents’ models and the statistical propertiesof the underlying data-generating process. We then apply our framework to two applications in asset pricing: (i) violations of uncovered interest rate parity at different horizons and (ii) momentum and reversal in equity returns. We find that constraints on the complexity of agents’ models can generate return predictability patterns that are consistent with the data.
Keywords: model complexity; subjective expectations; asset pricing
JEL Codes: G4; G12; F31; D84
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| model complexity (C52) | deviation from rational expectations (D84) |
| deviation from rational expectations (D84) | predictable patterns in returns (G17) |
| k < n (C69) | deviation from rational expectations (D84) |
| k < n (C69) | predictable patterns in returns (G17) |
| complexity of agents' models (C73) | slope coefficients of regressions (C29) |
| slope coefficients of regressions (C29) | return predictability (C53) |
| presence of constrained agents (D82) | impact on return predictability (G17) |