Grabbing Investor Attention with Limited Resources: A Study of Small Cap Firms’ Communication Channels
Bibliographic record
Abstract
This paper examines the communication strategies employed by small cap firms listed on the Alternative Investment Market (AIM) of the London Stock Exchange. These small cap firms have great discretion in choosing their communication channels with investors and evolve in an environment with few information intermediaries. We investigate the use of three communication channels – press releases, conference calls, and social media – specifically surrounding earnings announcements. Our findings indicate that small cap firms utilize these three communication channels infrequently. However, when announcing positive earnings news, small cap firms are more likely to employ these channels, suggesting that firms communicate opportunistically. We find a positive association between the use of communication channels, particularly of social media, and measures of investor attention. Interestingly, while the use of communication channels is associated with positive stock returns surrounding earnings announcements, social media usage prior to earnings announcements is linked to subsequent stock price reversals. These findings provide insights into the communication practices of small cap firms and their implications for investor attention and market efficiency.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".