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A Generalized Earnings‐Based Stock Valuation Model with Learning
Author(s) -
Jacoby Gady,
Paseka Alexander,
Wang Yan
Publication year - 2017
Publication title -
financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.621
H-Index - 47
eISSN - 1540-6288
pISSN - 0732-8516
DOI - 10.1111/fire.12128
Subject(s) - unobservable , valuation (finance) , earnings , earnings growth , risk premium , economics , econometrics , stock (firearms) , portfolio , growth stock , stock market , financial economics , actuarial science , finance , restricted stock , mechanical engineering , paleontology , horse , engineering , biology
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.