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Record W3123249774 · doi:10.1080/09603107.2010.505552

Capital structures in an emerging market: a duration analysis of the time interval between IPO and SEO in China

2010· article· en· W3123249774 on OpenAlexaff
Yang Ni, Shasha Guo, David E. A. Giles

Bibliographic record

VenueApplied Financial Economics · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of VictoriaBP (Canada)Queen's University
Fundersnot available
KeywordsInitial public offeringCapital structureMonetary economicsDebtLeverage (statistics)Pecking order theoryBusinessProfitability indexDebt ratioEquity (law)Financial systemFinanceEconomics

Abstract

fetched live from OpenAlex

We model the durations between firms’ ‘Initial Public Offerings’ (IPOs) and their subsequent ‘Seasoned Equity Offerings’ (SEOs) in China between 2001 and 2006. Our results 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 and pecking order theories. 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. Surprisingly, profitability is positively related to the conditional probability of equity financing, and market timing is an important consideration when Chinese firms undertake equity financing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.005
GPT teacher head0.184
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2010
Admission routes1
Has abstractyes

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