Topical orientations and investor perceptions of acquisitions
Bibliographic record
Abstract
Purpose This study investigates the subtle role of strategic communication in the context of mergers and acquisitions (M&As), focusing on how the topical orientation of managerial discourse influences investor perceptions and evaluations. Design/methodology/approach We develop a tailored dictionary of terms to conduct an in-depth examination of M&A conference calls, a relatively understudied form of voluntary disclosure. Using an algorithmic approach to linguistic analysis, we capture the strategic orientations of managerial discourse from a sample of 716 M&A call transcripts during the period 2013–2018. Findings This research shows that when top managers emphasize operational aspects over financial details during M&A calls, the acquiring firms are more likely to experience positive cumulative abnormal returns (CAR). However, overemphasizing operational aspects leads to negative CAR. It also shows that managerial focus on future prospects during these calls is positively associated with the acquirer CAR. Originality/value This study integrates insights from the acquisition literature with research in communications and linguistics, advancing the view that executive communication is not solely a mechanism for disclosing information but also a strategic act. In particular, it highlights that the orientation of communication is as consequential as its substantive content.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".