Asset Pricing with Adaptive Learning
AbstractWe 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 InfoPaper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 6223.
Date of creation: Apr 2007
Date of revision:
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Other versions of this item:
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-04-28 (All new papers)
- NEP-CBA-2007-04-28 (Central Banking)
- NEP-CFN-2007-04-28 (Corporate Finance)
- NEP-FMK-2007-04-28 (Financial Markets)
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