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Asset Pricing with Adaptive Learning

  • Carceles-Poveda, Eva
  • Giannitsarou, Chryssi

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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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 6223.

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Date of creation: Apr 2007
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Handle: RePEc:cpr:ceprdp:6223
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  1. Ellen R. McGrattan & Edward C. Prescott, 2003. "Average Debt and Equity Returns: Puzzling?," Levine's Working Paper Archive 506439000000000367, David K. Levine.
  2. Campbell, John & Shiller, Robert, 1988. "Stock Prices, Earnings, and Expected Dividends," Scholarly Articles 3224293, Harvard University Department of Economics.
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  6. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Adaptive learning in practice," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2659-2697, August.
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  8. Michele Boldrin & Lawrence J. Christiano & Jonas D.M. Fisher, 1999. "Habit persistence, asset returns and the business cycles," Working Paper Series WP-99-14, Federal Reserve Bank of Chicago.
  9. 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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  13. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
  14. Brock, William A., 1980. "Asset Prices in a Production Economy," Working Papers 275, California Institute of Technology, Division of the Humanities and Social Sciences.
  15. Timmermann, Allan, 1996. "Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning," Review of Economic Studies, Wiley Blackwell, vol. 63(4), pages 523-57, October.
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  19. repec:cup:macdyn:v:5:y:2001:i:2:p:272-302 is not listed on IDEAS
  20. Pok-sang Lam & Stephen G. Cecchetti & Nelson C. Mark, 2000. "Asset Pricing with Distorted Beliefs: Are Equity Returns Too Good to Be True?," American Economic Review, American Economic Association, vol. 90(4), pages 787-805, September.
  21. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
  22. Jermann, Urban J., 1998. "Asset pricing in production economies," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 257-275, April.
  23. Timmermann, Allan G, 1993. "How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 108(4), pages 1135-45, November.
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