Market reactions to corporate name changes: evidence from the Toronto Stock Exchange
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine stock price and trading volume reactions to name changes of the Toronto Stock Exchange listed companies. Previous studies present conflicting evidence on reactions to corporate name changes in US and other capital markets. Design/methodology/approach This study uses the event study methodology to calculate abnormal returns and trading volume around the announcement, approval, and effective dates of corporate name changes. It also contrasts abnormal returns between major and minor name changes, signaling focused and diversified strategies, accompanied with a ticker symbol change and without a ticker change, structural and pure name changes, as well as brand adoption and radical name changes. Findings Companies tend to experience a significant run-up in stock price in the period preceding the announcement of a name change. The stocks also show a significant positive abnormal return around the effective date. In addition, corporate name changes are associated with significant increases in trading volume for several days starting from the approval date. Most importantly, the type of a name change matters, as reflected in significance levels of abnormal return and trading volume reactions to various types of corporate name changes. Research limitations/implications The limitation of this study comes from the difficulty to precisely identify the date when the market learns about a possible corporate name change. Originality/value This study is the first to examine market reactions to name changes of Toronto Stock Exchange listed companies. Most importantly, whereas previous studies focus on the announcement day, this paper also considers the approval and effective days. It also contrasts responses between name changes accompanied with a new ticker and name changes without a ticker change.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".