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Discussion Paper Details

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Title: Asset Pricing with Adaptive Learning

Author(s): Eva Carceles-Poveda and Chryssi Giannitsarou

Publication Date: April 2007

Keyword(s): Adaptive learning, Asset pricing, Excess returns and Predictability

Programme Area(s): Financial Economics and International Macroeconomics

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.

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Bibliographic Reference

Carceles-Poveda, E and Giannitsarou, C. 2007. 'Asset Pricing with Adaptive Learning'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=6223