The Rich Get Richer and the Poor Get Poorer: Social Media and the Post-IPO Behavior of Investors in Biotechnology Firms: The Relationship with Twitter Volume
Bibliographic record
Abstract
This paper analyzes stock returns for biotechnology firms after initial public offering (IPO) and explores the effect of social media—specifically, Twitter—on these returns. The results indicate positive yet insignificant cumulative average abnormal returns (CAARs) of 1.97% in the first 25 days post-IPO and a decline of tens of percentage points over the following three years. However, after dividing the sample firms into two subsamples according to size, either under or over USD 500 million in market value, the overall results change dramatically. Firms with a market value lower than USD 500 million yield negative CAARs immediately following the IPO; however, this negative CAAR becomes significant only from day 50 onward. Firms with a market value over USD 500 million yield positive CAARs immediately following the IPO, which become significant from day 50, remaining so throughout the following year. These findings can be attributed to the limited duration of investors’ attention, which increases until the end of quiet period and, with small-sized firms, diminishes during the post-IPO years. An examination of Twitter activity and share returns demonstrates a robust correlation between the two, suggesting that investors’ attention to firms may be reflected in their Twitter usage.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".