The impact of cross listing on shareholder's return : an empirical study of Canadian mining companies cross listed on the Frankfurt Stock Exchange
Bibliographic record
Abstract
The Impact of Cross Listing on Shareholder's Return: An Empirical study of Canadian Mining Companies Cross-listed on the Frankfurt Stock Exchange by Ndubuisi Austin Chukwunyere This paper tests the impact of cross-listing on Canadian mining firm's shareholder's return.An event study is used to test abnormal return following the announcements of cross-listing event on the Frankfurt stock exchange.Cumulative Abnormal return around the cross-listing date is used as a proxy to test this impact.31 Canadian firms that are cross-listed on the Frankfurt stock exchange are collected through the period of 1989-2003 for this study.Canadian stocks react negatively to cross-listing on Frankfurt stock exchanges around the cross-listing date, at the 5% significance level.However over a relative long period Cross-listing in the Frankfurt stock exchange showed a less negative market reaction.Both reactions are however not significant.The test results support findings of previous studies that cross-listing provides some sort of benefit especially over time.Therefore, Canadian firms considering cross-listing on the Frankfurt stock exchange as a way to add value for its shareholders' should consider it despite the Canadian market reaction to this decision.This is because cross-listing provides many other benefits as highlighted in our literature review that on the long run will affect its share-price and thus shareholders return.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".