Working Paper: NBER ID: w28408
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 drive the 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 properties of 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; Expectations; Asset Prices
JEL Codes: D84; F31; G12; G4
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| limitations in cognitive and computational abilities (D91) | reliance on simplified models (C20) |
| reliance on simplified models (C20) | systematic deviations from rational expectations (D84) |
| number of factors (k) that agents can consider (C38) | expectations (D84) |
| expectations (D84) | asset prices (G19) |
| degree of model complexity mismatch (k vs. n) (C52) | extent of return predictability (G17) |
| short-term persistence in the data (C41) | momentum and reversal in returns (G17) |
| constrained agents (D10) | overall return predictability (G17) |