Reliability And Non-GAAP Adjustment Explanations
Bibliographic record
Abstract
Non-GAAP Financial Measures (NGFMs) have been in the academic spotlight for the last three decades, with a particular concern around the reliability of these measures. To address this reliability concern, regulators (including the Canadian Securities Administrators, CSA, and the U.S. Securities and Exchange Commission, SEC) have recently started requiring firms to provide a written explanation of their NGFM adjustments. However, to date, our understanding of how these explanations of NGFM adjustments influence investors is limited. This dissertation employs two methods to delve into the question of how explanations of NGFM adjustments influence investors' perceptions of reliability. First, I interview financial analysts, who reported that NGFMs are used to augment GAAP measures and stated their beliefs that NGFMs are important when evaluating the core operations of firms; however, they also spoke about a lack of consistency and transparency in NGFM adjustments which, in turn, serves to undermine their reliability. Second, I use an experiment to show that when firms provide an explanation for their NGFM adjustments, regardless of whether they are mandated by regulators or voluntarily provided, investors’ perception of their reliability increases. My findings also show that investors’ perception of the reliability of NGFM adjustments does not differ between positive versus negative adjustments. To conclude I find that explanations increase investors’ perceptions of the reliability of NGFM adjustments and financial information provided by public companies. My findings contribute important depth and understanding about investors’ perceptions of the reliability of NGFM adjustments. These findings have implications for accounting and finance academics, professionals, and standard-setters alike.
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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 | 0.015 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".