Effect of Earnings Management on Economic Value Added: A China Study
Bibliographic record
Abstract
Earnings management is the judgement exercised by managers in financial reporting, which can be used to mislead stakeholders about reported accounting numbers. Economic value added (EVA) is used to obtain the real value of shareholder wealth; however, EVA is based on financial statements and is used to measure the value of competing companies, which likely motivates managers to engage in earnings management regarding EVA. This paper thus addresses the association between earnings management and EVA in China and investigates whether earnings management influences a firm’s EVA regarding capital cost, providing investors with a method of determining the true value of enterprises. An analysis of earnings management is also presented based on data from 2003 to 2013 (excluding 2008). A significant positive relationship exists between earnings management through discretionary accruals (DAs) (Jones model, discretionary working capital accruals) and unadjusted EVA, a significant inverse relationship exists between earnings management through DAs (Jones model, current DAs, discretionary working capital accruals) and adjusted EVA (join adjusted items), a significant positive relationship exists between earnings management through DAs (current DAs) and adjusted EVA (join adjusted items and economic deprecation adjusted items), and a significant inverse relationship exists between earnings management through DAs (Jones model, discretionary working capital accruals) and adjusted EVA (join adjusted items and economic deprecation adjusted items).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".