Testing Factor-Model Explanations of Market Anomalies
By Kent D. Daniel, Sheridan Titman. 2012
164 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/d50dc67f55e32f041636ba9608da1df62cd2f44a
How researchers see this paper
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In Economics and Econometrics
- Zhaoguo Zhan
- Pavel Savor
- Mungo Wilson
- Kenneth R. Ahern
- F. Kleibergen
- Sophocles Mavroeidis
- Patrik Guggenberger
- Isaiah Andrews
In Finance
- Zhaoguo Zhan
- Pavel Savor
- Mungo Wilson
- Kenneth R. Ahern
- F. Kleibergen
- Sophocles Mavroeidis
- Patrik Guggenberger
- Christian Hansen
Citations in the corpus
- Earnings Announcements and Systematic Risk (2015, methodology): “To these 40 portfolios, we add 10 industry portfolios and our five portfolios based on firms’ earnings announcement betas, as advocated by Lewellen, Nagel, and Shanken (2010) and Daniel and Titman (2012).”
- Momentum Crashes (2010, unknown): “…lack of correlation of momentum portfolio returns with standard proxy variables for macroeconomic risk ( e.g., consumption growth) sharpens the puzzle still further (see, e.g. , Daniel and Titman (2011)) A number of behavioral theories of price formation proport to yield momentum as an implication.”
- Network Centrality and the Cross Section of Stock Returns (2013, background): “However, as argued by Lewellen, Nagel, and Shanken (2010) and Daniel and Titman (2012), the inherent factor structure of the size-B/M portfolios means that many additional factors are likely to correlate with these portfolios.”
- Unexplained factors and their effects on second pass R-squared’s (2015, methodology): “These pseudo-R(2)’s result from the expression in (10) but then applied to the regression of the three largest principal components on the observed proxy factors (prin.”
- Forest Through the Trees: Building Cross-Sections of Stock Returns (2020, unknown): “Furthermore, consistent with Daniel and Titman (2012), we find that standard unconditional sorts have a relatively low power in discriminating models and develop alternative test assets based on APTrees.”