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Return Predictability Under the Alternative

54 Pages Posted: 26 Aug 2012 Last revised: 3 Dec 2013

Marco Rossi

Texas A&M

Timothy T. Simin

Pennsylvania State University

Daniel R. Smith

Queensland University of Technology - School of Economics and Finance; Simon Fraser University; Financial Research Network (FIRN)

Date Written: November 22, 2013

Abstract

Long-horizon predictability is not a myth. We propose a new analytical standard error for predictive regressions that does not impose the null hypothesis that returns are unpredictable and exhibits substantial power gains relative to popular tests. Deriving the covariance matrix under the alternative hypothesis produces two new terms capturing the volatility of shocks to the regressor and their correlation with shocks to the prediction equation. Empirically, we show that failure to detect long-horizon predictability comes from lower power in tests derived under the null hypothesis. For many predictors, giving the alternative a chance allows short-run predictability to survive at long-horizons.

Keywords: Predictability, overlapping observations, analytical standard errors, size, power

JEL Classification: G12, C52

Suggested Citation

Rossi, Marco and Simin, Timothy T. and Smith, Daniel R., Return Predictability Under the Alternative (November 22, 2013). Available at SSRN: https://ssrn.com/abstract=2136047 or http://dx.doi.org/10.2139/ssrn.2136047

Marco Rossi

Texas A&M ( email )

360S Wehner
College Station, TX 77843-4218
United States

Timothy T. Simin

Pennsylvania State University ( email )

University Park, PA 16802
United States
814-865-3457 (Phone)

HOME PAGE: http://timsimin.net

Daniel Robert Smith (Contact Author)

Queensland University of Technology - School of Economics and Finance ( email )

GPO Box 2434
2 George Street
Brisbane, Queensland 4001
Australia
+61 7 3138 2947 (Phone)
+61 7 3138 2947 (Fax)

Simon Fraser University ( email )

8888 University Drive
Burnaby, British Colombia V5A 1S6
Canada
778-782-4675 (Phone)
778-782-4920 (Fax)

HOME PAGE: http://www.sfu.ca/~drsmith

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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