Real Options for IFRS-S1 and S2 2024 Mandatory Disclosures: An Alternative Approach to Capital Budgeting Valuation
Bibliographic record
Abstract
The new financial standards, IFRS S1 and S2, have not only modified the way financial reporting is presented to diverse stakeholders but have also increased uncertainty. These changes make traditional valuation methods inadequate. This article proposes the development of a valuation framework using Real Options Valuation (ROV), which incorporates the disclosures required by S1 and S2 as inputs to the valuation model. The framework proposes a quarterly decision rule for deferring investments, parameters aligned with the new sustainability disclosures, and notes in the financial statements proposed as voluntary reporting. The results show that, under regulatory uncertainty and its associated implications, the deferral option is a more effective technique than the Net Present Value method. For professionals responsible for the valuation process, the proposed model serves as a practical guide for applying the ROV within the capital budgeting process. For investors, it provides an additional element of transparency through disclosure and alignment with other existing accounting standards. This work lays the groundwork for future empirical applications as companies adapt to the implementation of new accounting standards and their associated reporting.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".