Financial performance effects of synthetic leases
Bibliographic record
Abstract
Abstract This paper examines selected financial performance measures of companies using synthetic leases. First, an example is provided of some advantages of using a synthetic lease. Synthetic lease announcements are then identified for retail and manufacturing sectors from 1997–2002 through Academic LexisNexis searches, and performance measures statistically compared from pre‐ to post‐disclosure for synthetic lease companies, relative to a control sample of companies. While observing some differences in interest coverage and profitability across samples in the year prior to lease disclosure, logistic regression results indicate that the measures do not statistically discriminate between synthetic lease and control companies. These findings suggest that the level at which companies use synthetic leases does not appear to significantly influence basic financial performance measures. Besides showing the little ‘real’ benefit of establishing such types of leases for non‐economic reasons, the results suggest that capital allocation decisions should focus on the substance rather than the form of such synthetic lease transactions. Copyright © 2003 Henry Stewart Publications
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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 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".