Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/177761
Authors: 
Gopalakrishna, Goutham
Year of Publication: 
2017
Series/Report no.: 
Quaderni - Working Paper DSE 1107
Abstract: 
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.
Subjects: 
Long run risk
Valuation risk
Machine Learning
Three pass filter
Media
Consumption
JEL: 
G12
E21
E44
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Working Paper

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