Moody's Approach to Global Standard Adjustments in the Analysis of Financial Statements for Non-Financial Corporations
Bibliographic record
Abstract
Moody's adjusts financial statements to better reflect the underlying economics of transactions and events and to improve the comparability of financial statements. We compute credit-relevant ratios using adjusted data and base our debt ratings, in part, on those ratios.This report discusses Moody's Standard Adjustments to financial statements prepared under US, Canadian and Japan accounting principles (these are collectively referred to as GAAP in this publication unless noted otherwise) and International Financial Reporting Standards (IFRS). Those adjustments we discuss herein may be unique to GAAP or IFRS but may also be applied to other accounting jurisdictions collectively termed “local GAAP” whenever it is appropriate to do so in order to make these more comparable to statements of non-financial corporations that report under either GAAP or IFRS.In addition to the Standard Adjustments, Moody's analysts may also make non-standard adjustments to financial statements for other matters to better reflect underlying economics and improve comparability with peer companies. For example, we may adjust financial statements to reflect estimates or assumptions that we believe are more suitable for credit analysis.With the use of Standard Adjustments, Moody's research includes, for each rated company, the nature and amount of all Standard Adjustments and those other adjustments that we make based on publicly available information. We also published key financial ratios reflecting the adjustments we make to financial statements. Our financial ratios in most cases do not contain complicated add backs to the numerators and denominators, but instead are based on fully adjusted sets of financial statements.Our adjustments do not imply that a company's financial statements fail to comply with GAAP or IFRS. Indeed, many of our adjustments are inconsistent with current accounting principles. Our goal is to enhance the analytical value of financial data for credit analysis and not to measure compliance with accounting rules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".