How Capital Leases Affect Firm Performance: An Analysis in the Shipping Industry
Bibliographic record
Abstract
This study examines the effects of capital lease arrangements on the operating performance of shipping firms as proxied by Return on Assets (ROA). The maritime industry is highly capital-intensive, often requiring substantial investments in fleet acquisition and maintenance, making ROA particularly relevant as it captures the effectiveness of firms in utilizing their leased and owned assets to generate operating income. As such, many firms rely on lease arrangements to access necessary resources while preserving liquidity and financial flexibility. Using an international sample of 209 shipping firms, we estimate fixed effects regressions to assess the relationship between lease intensity and performance of the shipping firms. The findings reveal that capital lease intensity is positively associated with operating performance, indicating that leasing can be a value-enhancing financing strategy in this sector. However, the performance benefits of capital leases diminish under IFRS 16 reporting, particularly for firms with higher leverage. These findings offer important implications for investors, regulators, and managers evaluating capital structure decisions and financial reporting strategies in capital-intensive industries post-IFRS 16 implementation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".