The supraview of return predictive signals

Jeremiah Green (), John R. M. Hand () and X. Frank Zhang ()
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Jeremiah Green: The Pennsylvania State University
John R. M. Hand: University of North Carolina–Chapel Hill
X. Frank Zhang: Yale University

Review of Accounting Studies, 2013, vol. 18, issue 3, No 4, 692-730

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: G12 G14 (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11142-013-9231-1

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