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Individual Assets and the SDF

Paper Session

Friday, Jan. 6, 2023 8:00 AM - 10:00 AM (CST)

Sheraton New Orleans, Napoleon D
Hosted By: American Finance Association
  • Chair: Seth Pruitt, Arizona State University

Time Series Variation in the Factor Zoo

Hendrik Bessembinder
,
Arizona State University
Aaron Burt
,
University of Oklahoma
Christopher Hrdlicka
,
University of Washington

Abstract

The ability of three- to six-factor models to explain the cross-section of stock returns varies substantially over time, providing scope for time-varying numbers of additional factors. We show that additional factors are relevant and non-redundant, as out-of-sample Sharpe ratios formed from principal components of factors identified in-sample are economically substantive and continue to increase up to more than twenty factor principal components. The numbers of significant factors are strongly related to variation in economic conditions and measures of diversity in firm characteristics. These results suggest that time variation in the number of significant factors reflects time-varying economic complexity.

Three Common Factors

Elena Andreou
,
University of Cyprus
Patrick Gagliardini
,
University of Lugano and Swiss Finance Institute
Eric Ghysels
,
University of North Carolina-Chapel Hill
Mirco Rubin
,
EDHEC Business School

Abstract

Hint: 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

Charles Clarke
,
University of Kentucky
Morteza Momeni
,
University of Kentucky

Abstract

This 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?

Serhiy Kozak
,
University of Maryland
Stefan Nagel
,
University of Chicago

Abstract

When 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.

Discussant(s)
Shrihari Santosh
,
University of Colorado Boulder
Dacheng Xiu
,
University of Chicago
Russell Wermers
,
University of Maryland
Mikhail Chernov
,
University of California-Los Angeles
JEL Classifications
  • G1 - Asset Markets and Pricing