Individual Assets and the SDF
Friday, Jan. 6, 2023 8:00 AM - 10:00 AM (CST)
- Chair: Seth Pruitt, Arizona State University
Three Common Factors
AbstractHint: these are not the Fama-French 3 factors and they are not even spanned by the Fama-French 5 factors. More importantly, they feature superior out-of-sample pricing performance compared to standard asset pricing models. What is ``common'' about these factors? We identify the factor space common between individual stocks and sorted portfolios - neither affected by time-varying betas nor by the sorting characteristics.
Testing Asset Pricing Models on Individual Stocks
AbstractThis paper tests asset pricing models using individual stocks as test assets, rather than sorted portfolios. Sorted portfolios have the severe limitation that the researcher must know, in advance, reliable predictors of expected returns. We show how to generate appropriately sized tests and verify that our tests have considerable test power. In simulations when the CAPM describes the population, our tests (correctly) reject the Fama and French (2015) six factor model 97.5% of the time, while our tests (incorrectly) reject the CAPM less than 5%. We apply our tests to seven leading factor models. We reject six of the seven leading models we test. The instrumented factor model of Kelly, Pruitt, and Su (2019) stands out as the most successful.
When Do Cross-Sectional Asset Pricing Factors Span the Stochastic Discount Factor?
AbstractWhen expected returns are linear in asset characteristics, the stochastic discount factor (SDF) that
prices individual stocks can be represented as a factor model with GLS cross-sectional regression slope
factors. Factors constructed heuristically by aggregating individual stocks into characteristics-based
factor portfolios using sorting, characteristics-weighting, or OLS cross-sectional regression slopes do
not span this SDF unless the covariance matrix of stock returns has a specific structure. These conditions are more likely satisfied when researchers use large numbers of characteristics simultaneously.
Methods to hedge unpriced components of heuristic factor returns allow partial relaxation of these
conditions. We also show the conditions that must hold for dimension reduction to a number of factors
smaller than the number of characteristics to be possible without having to invert a large covariance
matrix. Under these conditions, instrumented and projected principal components analysis methods
can be implemented as simple PCA on certain portfolio sorts.
University of Colorado Boulder
University of Chicago
University of Maryland
University of California-Los Angeles
- G1 - Asset Markets and Pricing