Factors Affecting Leveraging for Quoted Real Estate Development Companies in China
Bibliographic record
Abstract
Real estate companies in China need to undertake capital restructuring by deepening and broadening additional sources of funding such as real estate investment trusts, venture capital, mezzanine financing and commercial mortgage backed securities. This study is aimed at analyzing the factors that influence leveraging among quoted real estate development companies in China. A sample of 90 real estate companies quoted on Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange(SZSE) for the period covering 2005 to 2011 was selected however, due to information inadequacy, only 68 samples were analyzed using fixed effects regression. The results exhibited the following; management experience, company size, “guanxi” common in china meaning (personal/business relationship), company growth, company profitability, asset tangibility were positive and significant to leverage, whereas; interest amount was negative and significant, business risk and company age were negative and insignificant to the company leverage. In overall, the outcome of the research is in line with predictions from various documented theories and previous works.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".