Investor Perception, Market Reaction, and Post-Issue Performance in Bank Seasoned Equity Offerings
Bibliographic record
Abstract
Using a large sample of bank seasoned equity offerings (SEO) from 2002 to 2017, we first documented detailed descriptive statistics, and showed that nonperforming assets ratio, our primary measure of bank asset quality, reached the highest value immediately after the 2008 economic crisis, which also corresponds to a higher number of SEOs around these years because banks needed to recapitalize. The capital ratio, which is required to be at least at a minimum level (relative to risk-weighted assets) for banks by regulation, also increased after the economic crisis, which may be due to a higher requirement for banks as well as banks’ desire to hold more capital. The SEO announcement period abnormal stock-returns reached the lowest number around the economic crisis, as did the longer-run 6-month, post-SEO cumulative abnormal returns and buy-and-hold abnormal returns. Examining the differences between banks, we found, in both univariate and multivariate regression results controlling for other variables, that the bank capital ratio as at the time of the SEO announcement is significantly and positively associated with announcement period abnormal returns, while nonperforming assets ratio of the bank as at the time of SEO announcements is not. However, the nonperforming assets ratio as at the time of the SEO announcements had a significantly negative association with post-SEO, 6-month longer-run abnormal stock returns, while the capital ratio did not have any significant association. The nonperforming assets ratio as at the time of SEO announcement was also significantly and negatively related to bank return on assets 6 months and 12 months after SEO, while the capital ratio was not. Thus, investors appear to perceive a well-ingrained, well-publicized regulatory norm—the capital ratio—as indicative of value creation as at the time of bank SEO announcements, while loan asset quality, which may be relatively more opaque, may determine post-SEO performance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".