The DOI matches its Crossref record.
Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement
Abstract
Alternative ways of conducting inference and measurement for long-horizon forecasting are explored with an application to dividend yields as predictors of stock returns. Monte Carlo analysis indicates that the Hansen and Hodrick (1980) procedure is biased at long horizons, but the alternatives perform better. These include an estimator derived under the null hypothesis as in Richardson and Smith (1991), a reformulation of the regression as in Jegadeesh (1990), and a vector autoregression (VAR) as in Campbell and Shiller (1988), Kandel and Stambaugh (1988), and Campbell (1991). The statistical properties of long-horizon statistics generated from the VAR indicate interesting patterns in expected stock returns.
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In the citing paper
“We observe the slope coefficient for the contemporaneous regression on 𝑁𝑅 𝐵 (when ℎ = 0) at -1 . 03%, with a Hodrick (1992) 𝑡 -statistic of -2.70.”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Hodrick (1992)
In the citing paper
“Specifically, in the contemporaneous regression, the slope coefficient for the optimistic (or positive ) news ratio registers at 1.09% (0.74%) with a Hodrick (1992) 𝑡 -statistic of 5.55 (3.31).”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Hodrick (1992)
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Cited by
Sorted by citations
FAMA, E. F. (1991) · The Journal of Finance · doi:10.1111/j.1540-6261.1991.tb04636.x · Cited by 5,350
Predicting excess stock returns out of sample: Can anything beat the historical average?
Campbell, J. Y., & Thompson, S. B. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm055 · Cited by 2,919
“During the 1990s and early 2000s, research continued on the prediction of stock returns from valuation ratios [Kothari and Shanken (1997) and Ponti" and Schall (1998)] and interest rates [Hodrick (1992)].”
BackgroundThe Dog That Did Not Bark: A Defense of Return Predictability
Cochrane, J. H. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm046 · Cited by 1,227
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
Chen, J., Tang, G., Zhou, G., & Zhu, W. (2025) · arXiv · doi:10.48550/arxiv.2502.10008 · Cited by 6
Metadata
- Authors
- Hodrick, R. J.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Hodrick, Robert J.
- Venue
- Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 1992-07
- Volume
- 5
- Issue
- 3
- Pages
- 357-386
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W3124485385
- Cited by
- 1,523
- References
- 0
- Citations by year
- 2026: 38 · 2025: 40 · 2024: 32 · 2023: 38 · 2022: 42 · 2021: 60 · 2020: 53 · 2019: 52 · 2018: 61 · 2017: 57 · 2016: 54 · 2015: 71 · 2014: 64 · 2013: 70 · 2012: 69
- FWCI
- 24.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Review of Financial Studies (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Keywords
- dividend yield, expected stock returns, long-term forecasting, vector autoregression, dividend discount model
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