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Asset pricing with adaptive learning

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  • 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 behavior, for either model, since the algorithm converges fast to rational expectations. At the other end, constant gain learning may sometimes contribute towards explaining the stock price volatility and the predictability of excess returns in the endowment economy. However, in the production economy 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 alone cannot resolve the asset pricing puzzles observed in the data

Suggested Citation

  • Eva Carceles Poveda & Chryssi Giannitsarou, 2006. "Asset pricing with adaptive learning," Computing in Economics and Finance 2006 25, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:25
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    More about this item

    Keywords

    Asset pricing; adaptive learning; excess returns; predictability.;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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