Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 364

The DOI matches its Crossref record.

Corporate Combinations and Common Stock Returns: The Case of Joint Ventures

McCONNELL, JOHN J., NANTELL, TIMOTHY J.·1985·The Journal of Finance

Abstract

The gain to stockholders from mergers is well documented. However, there is little evidence as to whether the source of the gain is due to synergy or management displacement. Merger is just one of an almost limitless variety of ways in which firms combine resources to accomplish some objective. A joint venture is another. In addition to being of interest as an independent phenomenon, because the original managements of the parent firms remain intact under a joint venture, investigation of wealth gains from joint ventures provides an opportunity to isolate the management displacement hypothesis from the synergy hypothesis as the source of gains in corporate combinations. Our results are 1) there are significant wealth gains from joint ventures, 2) the smaller partner earns a larger excess rate of return while the dollar gains are more equally divided, and 3) the gains, scaled by resources committed, yield “premiums” similar to those in mergers. We are inclined to interpret our results as supportive of the synergy hypothesis as the source of gain from corporate combinations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1540-6261.1985.tb04970.x

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 364

Metadata

Authors
McCONNELL, J. J., & NANTELL, T. J.
Venue
The Journal of Finance
Publisher
Wiley
Published
1985-06
Volume
40
Issue
2
Pages
519-536
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
OpenAlex
W2023114801
Cited by
364
References
0
FWCI
7.74
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Corporate Combinations and Common Stock Returns: The Case of Joint Ventures. https://app.cochat.ai/papers/10.1111/j.1540-6261.1985.tb04970.x
The paper
McCONNELL, J. J., & NANTELL, T. J. (1985). Corporate Combinations and Common Stock Returns: The Case of Joint Ventures. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.1985.tb04970.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.