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Simple Forecasts and Paradigm Shifts

Authors

  • HARRISON HONG,

  • JEREMY C. STEIN,

  • JIALIN YU

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    • Harrison Hong is from Princeton University, Jeremy Stein is from Harvard University and the National Bureau of Economic Research, and Jialin Yu is from Columbia University. We are grateful to the National Science Foundation for research support. Thanks also to Patrick Bolton, John Campbell, Glenn Ellison, David Laibson, Sven Rady, Andrei Shleifer, Christopher Sims, Lara Tiedens, Pietro Veronesi, Jeff Wurgler, Lu Zhang, and seminar participants at Princeton, the University of Zurich, the University of Lausanne, the Stockholm School of Economics, the Norwegian School of Management, the Federal Reserve Board, the AFA meetings and the NBER corporate finance and behavioral finance meetings for helpful comments and suggestions.

ABSTRACT

We study the asset pricing implications of learning in an environment in which the true model of the world is a multivariate one, but agents update only over the class of simple univariate models. Thus, if a particular simple model does a poor job of forecasting over a period of time, it is discarded in favor of an alternative simple model. The theory yields a number of distinctive predictions for stock returns, generating forecastable variation in the magnitude of the value-glamour return differential, in volatility, and in the skewness of returns. We validate several of these predictions empirically.

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