Working Paper: CEPR ID: DP6223
Authors: Eva Carceles-Poveda; Chryssi Giannitsarou
Abstract: We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the effects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that recursive least squares learning has almost no effects on asset price behaviour, since the algorithm converges relatively fast to rational expectations. On the other hand, constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy. In the production economy, however, the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that, contrary to popular belief, standard self-referential learning cannot fully resolve the asset pricing puzzles observed in the data.
Keywords: Adaptive Learning; Asset Pricing; Excess Returns; Predictability
JEL Codes: D83; D84; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| recursive least squares learning (C45) | asset price behavior (G40) |
| constant gain learning (C73) | stock price volatility (G17) |
| constant gain learning (C73) | return volatility (G17) |
| constant gain learning (C73) | predictability of excess returns (G17) |
| higher equity price volatility under adaptive learning (G17) | perceived increase in risk (D81) |
| perceived increase in risk (D81) | higher equity premium (G19) |