Investor and Firm Perspectives on Impression Management in Earnings Press Releases: Insights from TSX Venture Exchange Firms
Bibliographic record
Abstract
The TSX Venture Exchange, one of the global leaders in providing opportunities to raise equity capital for small and emerging firms, plays an important role in the Canadian economy.However, TSX Venture Exchange firms have not yet received significant attention from academic scholars.This study examines 1,317 earnings press releases (EPRs) of TSX Venture Exchange firms to investigate whether, and how, low-visibility firms engage in impression management practices.It also explores investor perspectives on impression management by examining stock market and online investment discussion board reactions to apparent impression management.Overall, findings of this study indicate that low-visibility firms use various impression management strategies such as tone management, readability manipulation, causal reasoning, and the emphasis of positive performance by thematic manipulation, reinforcement, and repetition in their EPRs.Results of stock market reaction tests suggest that tone management and emphasis of positive performance positively affect cumulative abnormal return around the issue of EPRs; however, these effects dissipate or reverse in the longer-term.Similarly, online investment board participants are initially drawn to EPRs which emphasize positive performance and abnormally high positive tone around the issuance of EPRs.However, in the longer-term, discussion participants pay less attention to those EPRs and switch their interest to EPRs with higher reading complexity.
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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 | 0.006 |
| 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 | 0.001 |
| 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".