Asset Pricing with Adaptive Learning
Eva Carceles-Poveda () and
Chryssi Giannitsarou ()
No 6223, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 (search for similar items in EconPapers)
JEL-codes: D83 D84 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-cfn and nep-fmk
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Journal Article: Asset Pricing with Adaptive Learning (2008)
Working Paper: Asset pricing with adaptive learning (2006)
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