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Inferring Aggregate Market Expectations from the Cross Section of Stock Prices

Published online by Cambridge University Press:  11 April 2023

Turan G. Bali
Affiliation:
Georgetown University McDonough School of Business turan.bali@georgetown.edu
D. Craig Nichols
Affiliation:
Syracuse University Whitman School of Management dcnichol@syr.edu
David Weinbaum*
Affiliation:
Syracuse University Whitman School of Management
*
dweinbau@syr.edu (corresponding author)
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Abstract

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We introduce a new approach to estimating long-term aggregate discount rates using the cross section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than 2%. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We are grateful to an anonymous referee and Thierry Foucault (the editor) for constructive and insightful comments that greatly improved the article. We also thank Geert Bekaert, Michael Brandt, Hui Guo, Michael Halling, Volkan Muslu (CFMA discussant), Jim Ohlson, Sergei Sarkissian, Ivo Welch, Robert Whitelaw, Jeff Wurgler, Nir Yehuda, Tim Zhang (AAA discussant), and conference participants at the 2017 American Accounting Association meeting and the 2019 Conference on the Convergence of Financial and Managerial Accounting Research for helpful comments and suggestions, and Yan Li and David Ng for sharing their aggregate implied cost of capital data. David Weinbaum gratefully acknowledges research support from the Harris Fellowship in Finance. All errors remain our responsibility.

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