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Review of Accounting Studies

, Volume 18, Issue 3, pp 692–730 | Cite as

The supraview of return predictive signals

  • Jeremiah Green
  • John R. M. Hand
  • X. Frank Zhang
Article

Abstract

This study seeks to inform investment academics and practitioners by describing and analyzing the population of return predictive signals (RPS) publicly identified over the 40-year period 1970–2010. Our supraview brings to light new facts about RPS, including that more than 330 signals have been reported; the properties of newly discovered RPS are stable over time; and RPS with higher mean returns have larger standard deviations of returns and also higher Sharpe ratios. Using a sample of 39 readily programmed RPS, we estimate that the average cross-correlation of RPS returns is close to zero and that the average correlation between RPS returns and the market is reliably negative. Abstracting from implementation costs, this implies that portfolios of RPS either on their own or in combination with the market will tend to have quite high Sharpe ratios. For academics who seek to document that they have found a genuinely new RPS, we show that the probability that a randomly chosen RPS has a positive alpha after being orthogonalized against five (25) other randomly chosen RPS is 62 % (32 %), suggesting that the returns of a potentially new RPS need to be orthogonalized against the returns of some but not all pre-existing RPS. Finally, we posit that our findings pose a challenge to investment academics in that they imply that either US stock markets are pervasively inefficient, or there exist a much larger number of rationally priced sources of risk in equity returns than previously thought.

Keywords

Supraview Return prediction Academic discovery 

JEL Classification

G12 G14 

Notes

Acknowledgments

We appreciate valuable comments from Peter Algert, Suresh Govindaraj, Gilad Livne, Russell Lundholm, Jim Ohlson, Panos Patatoukas, Peter Pope, an anonymous referee, and workshop participants at City University London, the 2012 Citi Global Quant Conference, and the 2012 Review of Accounting Studies Conference. A full listing of the papers referenced in and used by this study is available from the authors on request.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jeremiah Green
    • 1
  • John R. M. Hand
    • 2
  • X. Frank Zhang
    • 3
  1. 1.The Pennsylvania State UniversityUniversity ParkUSA
  2. 2.University of North Carolina–Chapel HillChapel HillUSA
  3. 3.Yale UniversityNew HavenUSA

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