A Generalized Earnings‐Based Stock Valuation Model with Learning

We present a stock valuation model in an incomplete‐information environment in which the unobservable mean of earnings growth rate (MEGR) is learned and price is updated continuously. We calibrate our model to a market portfolio to empirically evaluate its performance. Of the 8.84% total risk premiu...

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Bibliographic Details
Published in:	The Financial review (Buffalo, N.Y.) Vol. 52; no. 2; pp. 199 - 232
Main Authors:	Jacoby, Gady, Paseka, Alexander, Wang, Yan
Format:	Journal Article
Language:	English
Published:	Knoxville Blackwell Publishing Ltd 01-05-2017
Subjects:	asset pricing Earnings equity risk premium filtering G12 G14 incomplete information Risk premiums Stocks Valuation
Online Access:	Get full text
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Summary:	We present a stock valuation model in an incomplete‐information environment in which the unobservable mean of earnings growth rate (MEGR) is learned and price is updated continuously. We calibrate our model to a market portfolio to empirically evaluate its performance. Of the 8.84% total risk premium we estimate, the earnings growth premium is 4.57%, the short‐rate risk contributes 3.38%, and the learning‐induced risk premium on the unknown MEGR is 0.89% (a nontrivial 10% of the total risk premium). This result highlights the significant learning effect on valuation, implying an additional risk premium in an incomplete‐information environment.
Bibliography:	We gratefully acknowledge helpful comments from two anonymous referees and the Editor, Srinivasan Krishnamurthy, as well as from Albert S. Kyle, Nancy Anderson, and conference participants at the Financial Management Association 2008 and 2009 annual meetings. Jacoby thanks the Bryce Douglas Professorship in Finance and the Social Sciences and Humanities Research Council of Canada for their financial support. All errors are our own.
ISSN:	0732-8516 1540-6288
DOI:	10.1111/fire.12128