Beyond earnings: do EBITDA reporting and governance matter for market participants?
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether formally disclosing an earnings before interests, taxes, depreciation, and amortization (EBITDA) number reduces the information asymmetry between managers and investors beyond the release of GAAP earnings. The paper also assess if EBITDA disclosure enhances the value relevance and the predictive ability of earnings. Design/methodology/approach The authors explore the interface between GAAP and non-GAAP reporting as well as the impact of corporate governance on the quality of non-GAAP measures. Findings Results suggest that EBITDA reporting is associated with greater analyst following and with less information asymmetry. The authors also document that EBITDA reporting enhances the positive relationship between earnings and stock pricing as well as future cash flows. Moreover, it appears that corporate governance substitutes for EBITDA reporting for stock markets. Hence, EBITDA helps market participants to better assess earnings valuation when a firm’s governance is weak. Inversely, when governance is strong, releasing EBITDA information has a much smaller impact on the earnings-stock price relation. Originality/value The authors revisit the issue of how corporate governance relates with earnings quality by considering the potentially confounding effect of EBITDA reporting; it appears that such reporting substitutes for governance in moderating the relation between governance and earnings quality.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".