DATA REVISIONS AND OUT-OF-SAMPLE STOCK RETURN PREDICTABILITY

Authors

  • HUI GUO

    1. Guo: Assistant Professor, Department of Finance and Real Estate, University of Cincinnati, 418 Carl H. Lindner Hall, PO Box 210195 Cincinnati, OH 45221-0195. Phone (513) 556-7077, Fax (513) 556-0979, E-mail hui.guo@uc.edu
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      I thank the editor, Dennis Jansen, an anonymous referee, and the conference participants at the 2006 FMA annual meeting in Salt Lake City. Bill Bock, Kamyar Nasseh, and Allison Rodean provided excellent research assistance. The first draft of the paper was finished when Guo was senior economist at the Federal Reserve Bank of St. Louis.


Abstract

It has been found that the consumption-wealth ratio (cay) constructed from revised data is a strong predictor of stock market returns. This paper shows that its out-of-sample forecasting power becomes substantially weaker if cay is estimated using information available at the time of forecast. The difference, which mainly reflects periodic revisions in consumption and labor income data, is consistent with the conjecture that cay is a theoretically motivated variable. That is, revised data outperform real-time data because the former have smaller measurement errors. Nevertheless, practitioners should be cautious when they need to use real-time cay as a forecasting variable. (JEL G10, G14)

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