Distance, information asymmetry, and mergers: evidence from Canadian firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of the distance between the acquiror and the target on merger outcomes. Design/methodology/approach The authors use the distance between acquiror and target headquarters for a sample of 134 Canadian mergers to proxy for the impact of information asymmetry due to distance. They use an ordinary least squares regression to examine the impact of this distance on the abnormal returns earned by the acquiror and the operating performance of the acquiror. They also use a logistic regression to test for the impact of distance on the choice of the medium of exchange. Findings The results suggest that a larger distance between the acquiror and the target is related to lower abnormal returns for the acquiror, poorer post‐merger operating performance, as well as to a greater use of stock as the medium of exchange. The results are robust to several alternate specifications. Research limitations/implications The findings of this paper extend existing research that suggests that distance affects investment decisions. Moreover, by analyzing the choice of the medium of exchange, this paper provides evidence that indicates that the distance matters due to its impact on information. As such, the paper suggests a potential empirical approach to measuring information asymmetry. Future research could help us better understand the role of distance in various other aspects of corporate decision making. Originality/value This paper, by analyzing a sample of Canadian firms, provides an out‐of‐sample test for prior research that has focused almost exclusively on US firms. Moreover, by looking at the choice of the medium of exchange, it provides direct evidence that distance affects corporate decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".