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Learning and Stock Market Volatility

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Author Info

  • Klaus Adam

    ()
    (Research Department CEPR and European Central Bank)

  • Albert Marcet
  • Juan Pablo Nicolini

Abstract

Introducing learning into a standard asset pricing model improves considerably its empirical performance. In a model of learning where today's stock price is determined by the expectation of tomorrow's stock price, the dynamics of expectations and actual price are such that the market has inertia. If the market has been increasing it will have a tendency to increase further, thereby generating large and persistent deviations of asset prices from fundamental values. For overvalued asset prices the model predicts the possibility of sudden and strong price decreases, i.e., 'stock market crashes', but no symmetric stock market increases in the presence of undervalued asset prices. These features emerge even though the deviations of agents' price expectations from perfectly rational return forecasts would be hard to detect given available sample sizes. Using a calibrated asset pricing model with habit persistence and learning, we can match the following quarterly U.S. asset pricing facts: the mean and volatility of stock returns; the mean, volatility, and autocorrelation of the price dividend ratio; and the average bond returns (equity premium). Consistent with empirical studies, the learning model also predicts that the price dividend ratio has predictive power for stock returns over the medium term (but not the short-term) and is unrelated to future fundamentals. The same model under rational expectations generates insufficient volatility and auto-correlation of the price dividend ratio and implies that the price dividend ratio is unrelated to future stock returns. The learning and rational expectations models both predict too much volatility of the short-term real interest rate, although the learning model performs somewhat better on this account.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 15.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:15

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Web page: http://comp-econ.org/
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Cited by:
  1. Adam, Klaus & Marcet, Albert, 2009. "Internal Rationality and Asset Prices," CEPR Discussion Papers 7498, C.E.P.R. Discussion Papers.
  2. George W. Evans, 2011. "Comment on "Natural Expectations, Macroeconomic Dynamics, and Asset Pricing"," NBER Chapters, in: NBER Macroeconomics Annual 2011, Volume 26, pages 61-71 National Bureau of Economic Research, Inc.
  3. Eva Carceles Poveda & Chryssi Giannitsarou, 2006. "Asset pricing with adaptive learning," Computing in Economics and Finance 2006, Society for Computational Economics 25, Society for Computational Economics.
  4. Klaus Adam & Pei Kuang & Albert Marcet, 2011. "House Price Booms and the Current Account," NBER Chapters, in: NBER Macroeconomics Annual 2011, Volume 26, pages 77-122 National Bureau of Economic Research, Inc.
  5. Wiliam Branch & George W. Evans, . "Learning about Risk and Return: A Simple Model of Bubbles and Crashes," University of Oregon Economics Department Working Papers, University of Oregon Economics Department 2008-1, University of Oregon Economics Department.
  6. Klaus Adam & Albert Marcet, 2011. "Internal Rationality, Imperfect Market Knowledge and Asset Prices," CEP Discussion Papers dp1068, Centre for Economic Performance, LSE.
  7. Francesco Caprioli & Pietro Rizza & Pietro Tommasino, 2012. "Optimal fiscal policy when agents fear government default," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 859, Bank of Italy, Economic Research and International Relations Area.
  8. Klaus Adam & Albert Marcet, 2010. "Booms and Busts in Asset Prices," IMES Discussion Paper Series 10-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
  9. Eva Carceles-Poveda & Chryssi Giannitsarou, 2007. "Online Appendix to Asset Pricing with Adaptive Learning," Technical Appendices carceles08, Review of Economic Dynamics.

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