The Impact of Market Timing on European Firms’ Capital Structure: RLBOs vs. IPOs
Bibliographic record
Abstract
Our study compares the impact of market timing on the capital structure of reverse leveraged buyouts (RLBOs) and initial public offerings (IPOs). Our sample is made up of 210 RLBOs and 210 public companies listed between 1995 and 2015 and linked by size (turnover) and industry (based on the first two digits of the SIC code). Our results show that the impact of market timing measures on capital structure is different between RLBOs and public companies. In accordance with Baker and Wurgler (2002) and others, these measures have a negative and significant effect on the capital structure of the two types of companies. This significance is persistent ten years after the IPO for public companies and only three years after the IPO for RLBOs. RLBOs rebalance the market timing effect on their capital structures much more quickly and therefore move toward the target debt ratio more quickly than their counterparts. These results challenge the robustness and generality of Baker and Wurgler’s (2002) market timing theory. The capital structure of RLBOs seems to be better explained by the characteristic variables of companies suggested by the theory of trade-off.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".