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Cited by 373

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Testing the Predictive Power of Dividend Yields

GOETZMANN, WILLIAM N., JORION, PHILIPPE·1993·The Journal of Finance

Abstract

This paper reexamines the ability of dividend yields to predict long‐horizon stock returns. We use the bootstrap methodology, as well as simulations, to examine the distribution of test statistics under the null hypothesis of no forecasting ability. These experiments are constructed so as to maintain the dynamics of regressions with lagged dependent variables over long horizons. We find that the empirically observed statistics are well within the 95% bounds of their simulated distributions. Overall there is no strong statistical evidence indicating that dividend yields can be used to forecast stock returns.

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Crossref · 10.1111/j.1540-6261.1993.tb04732.x

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Metadata

Authors
GOETZMANN, W. N., & JORION, P.
Venue
The Journal of Finance
Publisher
Wiley
Published
1993-06
Volume
48
Issue
2
Pages
663-679
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
OpenAlex
W2153507057
Cited by
373
References
26
FWCI
11.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Testing the Predictive Power of Dividend Yields. https://app.cochat.ai/papers/10.1111/j.1540-6261.1993.tb04732.x
The paper
GOETZMANN, W. N., & JORION, P. (1993). Testing the Predictive Power of Dividend Yields. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.1993.tb04732.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.