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Lock-in of extrapolative expectations in an asset pricing model Author info | Abstract | Publisher info | Download info | Related research | Statistics Kevin J. Lansing
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This paper examines an agent's choice of forecast method within a standard asset pricing model. To make a conditional forecast, a representative agent may choose one of the following: (1) a rational (or fundamentals-based) forecast that employs knowledge of the stochastic process governing dividends, (2) a constant forecast based on a simple long-run average of the forecast variable, or (3) a time-varying forecast that extrapolates from the last observation of the forecast variable. I show that a representative agent who is concerned about minimizing forecast errors may inadvertently become "locked-in" to an extrapolative forecast. In particular, the initial use of extrapolation alters the law of motion of the forecast variable so that the agent perceives no accuracy gain from switching to one of the alternative forecast methods. Under extrapolative expectations, the model can generate excess volatility of stock prices, time-varying volatility of returns, long-horizon predictability of returns, bubbles driven by optimism about the future, and sharp downward movements in stock prices that resemble market crashes. All of these features appear to be present in long-run U.S. stock market data.
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Paper provided by Federal Reserve Bank of San Francisco in its series Working Papers in Applied Economic Theory with number
2004-06.
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Keywords: Stock - Prices Forecasting Other versions of this item:
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references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
Wiliam Branch & George W. Evans, 2006.
"Asset Return Dynamics and Learning ,"
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Kevin J. Lansing, 2007.
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Kevin J. Lansing, 2007.
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