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 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 | 0.008 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".