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Asset pricing with adaptive learning

  • Eva Carceles Poveda
  • Chryssi Giannitsarou

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

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 25.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:25
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  1. Kaushik Mitra & Seppo Honkapohja, 1999. "Learning with Bounded Memory in Stochastic Models," Computing in Economics and Finance 1999 221, Society for Computational Economics.
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  18. repec:cup:macdyn:v:5:y:2001:i:2:p:272-302 is not listed on IDEAS
  19. Brennan, Michael J. & Xia, Yihong, 2001. "Stock price volatility and equity premium," Journal of Monetary Economics, Elsevier, vol. 47(2), pages 249-283, April.
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