Determinants of Capital Structure and Speed of Adjustment: Evidence from Iran and Australia
Bibliographic record
Abstract
This study investigated different companies’ capital structures using a comparative approach in a developing country (Iran) and a developed country (Australia). The purpose of this study was to identify the factors affecting the capital structure based on the company's characteristics in Iran and Australia. The main characteristics of the companies used in this research are mainly based on the variables used in Pecking order theory and the Trade-off theory namely tangibility, firm size, profitability, and business risk. Three other variables including liquidity, asset utilization ratio and speed of adjustment were also investigated. Two indicators of total debt ratio and long-term debt ratio have been used as corporate leverage index. The population of this study included 178 Iranian companies listed on Iran's stock exchange and 187 Australian companies listed on Australia’s stock exchange from 2009 to 2015. To test the hypotheses, Panel data and Eviews software were used. To ensure robustness of the results, the speed of adjustment was estimated using GMM and OLS (with fixed and random effects).The results of this study showed that dynamic trade-off theory could better explain the changes in capital structure in Iran and Australia. The results also revealed significant differences in factors affecting the capital structure in Iran and Australia.
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 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 | 0.003 |
| 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 | 0.000 | 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".