Persuasion in Earnings Calls: A Diachronic Pragmalinguistic Analysis
Bibliographic record
Abstract
This study investigates persuasive language in earnings calls. These are routine events organized by companies to report their quarterly financial results. The analysis is based on the earnings calls of 10 companies in the third quarter of 2009, when financial markets were still suffering from the global financial crisis, and the third quarter of 2013 when markets had largely recovered. Earnings call transcripts were compiled in two parallel corpora (Crisis Corpus and Recovery Corpus), thus providing a diachronic perspective. Semantic annotation software was used to extract pragmalinguistic resources of persuasion. The Crisis Corpus had a higher frequency of persuasive items, as executives often emphasized progress and future hopes. However, the types of items were largely the same across the corpora. This suggests a well-consolidated linguistic protocol within this discourse community that transcends financial performance. The findings offer insights into how earnings call participants use persuasive language strategically to achieve their distinct professional objectives as responsible providers of information (executives) versus discerning seekers of information (analysts).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".