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Asset Pricing: Cross-section of Returns

Paper Session

Sunday, Jan. 5, 2020 8:00 AM - 10:00 AM (PDT)

Manchester Grand Hyatt, Seaport DE
Hosted By: American Finance Association
  • Chair: Serhiy Kozak, University of Maryland

Long-Term Discount Rates do not Vary Across Firms

Matti Keloharju
,
Aalto University
Juhani Linnainmaa
,
University of Southern California
Peter Nyberg
,
Aalto University

Abstract

Long-term expected returns appear to vary little, if at all, in the cross section of stocks. We devise a bootstrapping procedure that injects small amounts of variation into expected returns and show that even negligible differences in expected returns, if they existed, would be easy to detect. Markers of such differences, however, are absent from actual stock returns. Our estimates are consistent with production-based asset pricing models such as Berk, Green, and Naik (1999) and Gomes, Kogan, and Zhang (2003) in which firms’ risks change over time. We show that long-term reversals in stock returns are the consequence of the rapid convergence in expected returns. Our results imply stock market anomalies have only a limited effect on firm valuations.

Estimating the Anomaly Baserate

Alexander Chinco
,
University of Illinois
Andreas Neuhierl
,
University of Notre Dame
Michael Weber
,
University of Chicago

Abstract

The academic literature contains literally hundreds of variables that seem to predict the cross-section of expected returns. This so-called `anomaly zoo' has caused many to question whether researchers are using the right tests for statistical significance. But, here's the thing: even if a researcher is using the right tests, he will still be drawing the wrong conclusions from his analysis if he is starting out with the wrong priors---i.e., if he is starting out with incorrect beliefs about the ex ante probability of discovering a tradable anomaly prior to seeing any test results. So, what are the right priors to start out with? What is the correct anomaly baserate? We propose a new statistical approach to answer this question. The key insight is that, under certain conditions, there's a one-to-one mapping between the ex ante probability of discovering a tradable anomaly and the best-fit tuning parameter in a penalized regression. When we apply our new statistical approach to the cross-section of monthly returns, we find that the anomaly baserate has fluctuated substantially since the start of our sample in May 1973. The ex ante probability of discovering a tradable anomaly was much higher in 2003 than in 1990. As a proof of concept, we construct a trading strategy that invests in previously discovered predictors and show that adjusting this strategy to account for the prevailing anomaly baserate boosts its performance.

Operating Hedge and Gross Profitability Premium

Leonid Kogan
,
Massachusetts Institute of Technology
Jun Li
,
University of Texas-Dallas
Harold Zhang
,
University of Texas-Dallas

Abstract

In this paper we explore the hedging effect induced by variable costs in production, and its impact on fundamental risk of firm cash flows and stock returns. The hedging effect varies across firms and is weaker for more profitable firms. This leads to more profitable firms having a higher exposure to aggregate profitability shocks, giving rise to a gross profitability premium. Our model captures coexistence of the negatively correlated gross profitability and value factors, addressing an empirical pattern that poses a challenge to the models relying on operating leverage as the primary source of the value premium.

The Short Duration Premium

Andrei Goncalves
,
University of North Carolina-Chapel Hill

Abstract

Stocks of firms with cash flows concentrated in the short-term (i.e., short duration stocks) pay a large premium over long duration stocks. I empirically demonstrate this premium: (i) is long-lived and strong even among large firms; (ii) subsumes the value and profitability premia; and (iii) exposes investors to variation in expected returns, especially in times when the premium is high. These facts are consistent with an intertemporal model in which the marginal (long-term) investor dislikes expected return declines as they lead to lower expected wealth growth. The model also captures the positive relation between risk premia and bond duration.
Discussant(s)
Shrihari Santosh
,
University of Maryland
Francisco Barillas
,
University of New South Wales
Erik Loualiche
,
University of Minnesota
Michael Weber
,
University of Chicago
JEL Classifications
  • G1 - General Financial Markets