Asset Pricing with Adaptive Learning

40 Pages Posted: 22 May 2008

See all articles by Eva Carceles-Poveda

Eva Carceles-Poveda

SUNY at Stony Brook University - College of Arts and Science - Department of Economics

Chryssi Giannitsarou

University of Cambridge - Faculty of Economics; Centre for Economic Policy Research (CEPR)

Date Written: April 2007

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 Classification: D83, D84, G12

Suggested Citation

Carceles-Poveda, Eva and Giannitsarou, Chryssi, Asset Pricing with Adaptive Learning (April 2007). , Vol. , pp. -, 2007. Available at SSRN: https://ssrn.com/abstract=1135463

Eva Carceles-Poveda (Contact Author)

SUNY at Stony Brook University - College of Arts and Science - Department of Economics ( email )

Stony Brook, NY 11794
United States

Chryssi Giannitsarou

University of Cambridge - Faculty of Economics ( email )

Austin Robinson Building
Sidgwick Avenue
Cambridge, CB3 9DD
United Kingdom

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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