Impact of Market Timing on Corporate Capital Structure: Evidence from the UK IPO Market
Bibliographic record
Abstract
This study examines the impact of market timing on corporate capital structure throughout the analysis of the IPO market in the UK.It should be noted that due to its cyclical activity, the IPO market is considered as a natural place to detect market timing effects.In line with previous literature, this study considers the hot and cold classification as the equity timing measure.Market timers are identified as firms that go public in hot markets.Hot markets are characterised by periods of unusually high IPO volume and underpricing, usually on the back of the more favourable market conditions.On the contrary, cold markets are portrayed by opposite market conditions.The main findings of this study show that, in the offering year, hot-market firms issue more equity and experience a larger decrease in their leverage ratios in comparison with cold-market firms.However, right after going public, hotmarket firms start to increase their leverage ratios at a higher pace than their cold-market counterparts.Five years following the IPO, the difference in the leverage ratio when compared to the pre-issue level becomes slightly higher for cold-market firms, suggesting that the changes on leverage stop being driven by the market timing factor.These findings are consistent with the view that the impact of market timing on capital structure is not persistent over time.Different econometric models were computed to test the research hypotheses.Also, the Huber-White estimator was applied to correct the error structure, ensuring that our findings were not biased by heteroscedasticity and error correlation.In addition, industry-fixed effects were included to control for cross-sectional heterogeneity.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".