The Effects on Financial Leverage and Performance: The IFRS 16
Bibliographic record
Abstract
This paper analyses the potential impacts of the introduction of a new accounting standard, International Financial Reporting Standard 16 (IFRS 16) – Leases, on financial leverage and performance of entities. This new accounting standard was introduced on 13 January 2016, and will become effective on 1 January 2019; it will have material impacts on the financial statements of listed companies adopting IFRS and change the basic principles of the current accounting system. Our aim is to estimate the impacts of the application of IFRS 16 on listed issuers of financial statements and the different impacts that the new standard could have in different activity sectors. This research estimates the effects of IFRS 16 on the ratios of debt/total assets, EBITDA/revenues and debt/equity. The conclusions summarize the effects on entity performance and net financial position. The research shows that in the financial statements of the lessee, there will be important changes. In particular, in the balance sheet, there will be an increase in lease assets, an increase in financial liabilities and a decrease in equity, while in the income statement, there will be an increase in EBITDA and an increase in finance costs. The impact of the application of IFRS 16 will be different depending on the use of operating lease contracts among the different business sectors. Leases are an important and flexible source of financing; listed companies, using IFRS and U.S. GAAP, are estimated to have around US$ 3.3 trillion in lease commitments. Finally, this study aims to analyse the possible impacts of communication of entities, focusing on alternative performance measures.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".