Capital Structures in an Emerging Market: A Duration Analysis of the Time Interval Between IPO and SEO in China
Bibliographic record
Abstract
We model the durations between firms’ “Initial Public Offerings” (IPOs) and their subsequent “Seasoned Equity Offerings” (SEOs) in China during the period from 1 January 2001 to 1 July 2006. Duration analysis is applied by using the nonparametric Kaplan-Meier estimator of the hazard function, and parametric accelerated failure time models with time-varying covariates. The results of this analysis have important implications for the capital structure in emerging markets. Our evidence on financing decisions in China contradicts the predictions of both the trade-off theory and the pecking order theory. Firms do not issue equity after debt financing to offset the deviation from the target leverage ratio. Profitability is negatively related to debt ratios. Limited access to the corporate bond market and the privilege of the low effective tax rate that local governments give to firms have increased the cost of debt and decreased the benefit of debt, and make firms in China under-utilize the tax shield of debt. The most surprising finding is that profitability is positively related to the conditional probability of equity financing. Firms may intentionally manipulate the earnings to minimize the adverse section costs associated with equity financing and to meet the earnings requirement of China Securities Regulatory Commission set for SEO qualifications. Market timing is an important consideration when firms in China undertake equity financing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".