Bibliographic record
Abstract
This paper investigates non-GAAP financial measures voluntarily reported by Canadian companies listed on Toronto stock exchange (TSX) and Toronto Ventures Exchange (TSXV) for the year 2017. Non-GAAP measures are those that do not adhere to the requirements of generally accepted accounting principles (GAAP) and are used to communicate those aspects of firms’ operations which the firms see as relevant for the users of financial statements. This study is an exploratory research which describes current firm practices in reporting non-GAAP financial measures among three industry groups, namely Real Estate, Blockchain/Cryptocurrency and Cannabis firms. This paper also assesses the quality of non-GAAP financial disclosures in accordance with the regulatory guidance. The study is motivated by recent regulatory proposals issued by the Canadian Securities Administrators (CSA), under the National Instrument NI 52-112 and by the Accounting Standards Board (AcSB) pertaining to reporting non-GAAP performance measures. The main contribution of this study is a detailed content analysis of a sample of Canadian firms. My analysis of hand collected data from the Management Discussion and Analysis (MD&A) indicates a plethora of reported “non-GAAP financial measures” disclosed by companies. The analysis also indicates that firms are falling short on parameters such as understandability, comparability, standardization, consistency and persistence of non-GAAP financial measures which are essential under the existing guidelines, and that regulation of non-GAAP financial measures would be beneficial. The study’s findings may be relevant to regulators for formulating guidance on reporting non-GAAP measures and identifies areas of potential future studies in the area of non-GAAP financial measures. Keywords: Non-GAAP financial measures, Non-GAAP earnings, Pro forma earnings, Non-IFRS measures, Street earnings, Core earnings, Adjusted earnings and NI 52-112.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".