Is Firms’ Social Media Engagement Informative about Firm Performance?
Bibliographic record
Abstract
Abstract\nIn this paper, I examine whether the volume of a firm’s tweets and its followers’ engagement is informative to capital market participants and financial intermediaries, namely investors and analysts. My data comprises of 178,236 firm-quarters (46,449 Tweet firm-quarters) and approximately 17.50 million firm-initiated tweets collected from the Primary Twitter sites of 2,229 public US firms between 2006 and 2017. I find that the volume of a firm’s tweets and the followers’ engagement during a quarter predicts the firm value during that period. The results also suggest that changes in tweet (engagement) volume are informative to investors and the information gets impounded in the stock prices concurrently. I also find evidence that followers’ engagement is more informative than the firm’s tweet volume for predicting firm-value. My findings further indicate that analysts may be using this additional information in the firm’s tweet (engagement) volume to make more accurate earnings and sales forecast, which reduces the Tweet firm’s unexpected earnings and unexpected sales growth. In additional analysis, I find that the level of tweets (engagement) helps predict a firm’s earnings and sales whereas changes in tweet (engagement) volume incrementally explain the firm’s sales growth and this may be the source of additional information to investors and 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".