The Impact of Foreign Direct Investment on Financial Performance: Results from the Mergers and Acquisitions (M&A) Experience of Canadian Firms from 1999 to 2005.
Bibliographic record
Abstract
Mergers & Acquisition is an important strategy for higher market share, rapid market penetration and economies of scale. Trans-border M&A is an important part of the annual FDI (Foreign Direct Investment) flow. In recent years, Canada has witnessed an impressive number of mergers and acquisitions. The popular belief in connection with the subject of mergers and acquisitions is relatively mixed. However, when the acquirer is non-Canadian company especially from the countries other than the USA, Canadians express negative view on the M & A deals. In this study, we analyze the financial performance of pre- and post-acquisition of 95 mergers and acquisitions that took place in Canada between 1999 and 2005. These mergers and acquisitions have all been made by companies that are not Canadians. The results show that the post-acquisition financial performance is substantially the same as the one obtained pre-acquisition. We use the average earnings per share as a tool for calculating financial performance. In addition to measuring the change in earnings per share through pre- and postacquisition, we conduct segmentation by industry, size of investment, business experience and nationality of the buyer to understand whether these factors can explain the success or failure of a merger or acquisition transaction. In light of the results, only the industry and business experience affect, to a certain level, the chances of successful mergers and acquisitions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".