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Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
Quaderni - Working Paper DSE No. 1107
Verlag: 
Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), Bologna
Zusammenfassung: 
This paper tests the long run risk and valuation risk model using a robust estimation procedure. The persistent long run component of consumption growth process is proxied by a news based index that is created using a random forest algorithm. This news index is shown to predict aggregate long term consumption growth with an R-square of 57% and is robust to inclusion of other commonly used predictors. I theoretically derive an estimatable bias term in adjusted Euler equation of the model that arises due to measurement error in consumption data and show that this bias term is non-zero. Using a three pass estimation procedure that accounts for this bias, I show that the long run risk and valuation risk model fails to explain cross section of equity returns. This contrasts to the results from regular two pass Fama-MacBeth estimation procedure that implies that the same model explains the cross section of asset returns with statistically significant risk premia estimates.
Schlagwörter: 
Long run risk
Valuation risk
Machine Learning
Three pass filter
Media
Consumption
JEL: 
G12
E21
E44
Persistent Identifier der Erstveröffentlichung: 
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Dokumentart: 
Working Paper

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