Headline Impression Management in the Earnings Press Releases of TSX Venture Exchange Firms*
Bibliographic record
Abstract
ABSTRACT This paper explores firms traded on the Toronto Stock Exchange (TSX) Venture Exchange and their voluntary disclosure practices by focusing on earnings press releases (EPRs). We compare the characteristics of EPR issuers and non‐issuers and investigate how the former group uses headline impression management in their EPRs to highlight firm performance. More precisely, we examine emphasis and tone management techniques in the headlines of over 1,300 EPRs by TSX Venture Exchange (TSX‐V) firms. Our results show that the main determinants of the EPR disclosure choice are the achievement of positive revenue, an increasing trend in firm market value, and industry type. We find that EPR issuers reinforce and repeat positive results in the headlines of EPRs and use positive tone management to highlight positive financial performance. Our results confirm the association between firm performance and strategic placement of performance results, while illustrating that the strength of this association varies by industry and by EPR characteristics such as EPR length and numerical intensity. Overall, this paper sheds light on TSX‐V firms, their disclosure practices, and potential violations of recommendations from regulators regarding avoiding exaggerated or promotional language in press releases.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".