The Supraview of Return Predictive Signals

Abstract:     
This study speaks to investment academics and practitioners by describing and analyzing the population of return predictive signals (RPS) publicly identified during the period 1970-2010. Our supraview brings to light a number of new facts about the population of RPS, including that more than 330 signals have been discovered and reported; the properties of newly discovered RPS remain stable over time; and RPS with higher mean returns have not only larger standard deviations of returns, but higher Sharpe ratios too. Using a sample of RPS, we estimate the average signed (absolute) cross-correlation of returns in the population of RPS to be just 0.05 (0.25). Abstracting from implementation costs, we show that this low of an average signed cross-correlation in RPS returns means that in theory an optimal portfolio of all RPS can have an equally-weighted (value-weighted) annualized Sharpe ratio as large as 3.0 (4.5). We also show that the probability that a given RPS has a positive alpha after being orthogonalized against five (25) other randomly chosen RPS is 62% (32%). Our study suggests that practitioners can expect to create value for their clients by hunting down new sources of alpha, and that academics testing for the existence of a new RPS do not need to orthogonalize the returns of that RPS against all pre-existing RPS. However, our findings also pose a challenge to academic theorists, since they imply that either U.S. stock markets are pervasively inefficient, or there exist a much larger number of rationally priced sources of risk in equity returns than ever previously thought.

Number of Pages in PDF File: 49

Keywords: Return predictive signals, supraview, efficient markets

JEL Classification: G12, G14

Accepted Paper Series