WHAT DRIVES SECURITY ISSUANCE DECISIONS: MARKET TIMING, PECKING ORDER, OR INFORMATION ASYMMETRY?*
Bibliographic record
Abstract
We study whether firms ’ financing choice is driven by market timing, pecking order or information asymmetry. Using a sample of debt and equity issues and share repurchases of Canadian firms during 1998-2007, we find that firms are more likely to issue (repurchase) equity when their shares are overvalued (undervalued), and post-announcement long-run returns are lower for overvalued firms. These findings give support to the market timing theory. We also find support for the pecking order theory which predicts that firms prefer debt to equity financing unless they are financially constrained. The effects of market timing and pecking order interact: firms are most likely to time the market in issuing or repurchasing equity when they are least financially constrained, and pecking order is most likely to hold among undervalued firms. We find no support for the Myers-Majluf theory of information asymmetry which predicts that firms issue equity when the degree of information asymmetry is low. The overall conclusion is that public financing choice is more driven by market timing than by information asymmetries. This is in line with previous findings from surveys amongst Chief Financial Officers, such as Graham and Harvey (2001).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".