Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies
Bibliographic record
Abstract
Accounting manipulation undermines the integrity of financial reporting and can distort key performance indicators, yet its quantitative effects on accounting quality (AQ) and value-related metrics remain underexplored. This study analyses U.S. publicly traded firms involved in accounting manipulation between 2017 and 2019, comparing them with matched non-manipulative industry peers to assess differences in AQ. It also examines potential links between manipulation-related AQ distortions and changes in Economic Value Added (EVA), stock prices, trading volumes, and dividend payouts. The sample includes 57 manipulation-affected firms and 57 matched controls, identified through SEC enforcement filings and the Violation Tracker database. Financial and stock data were sourced from EDGAR, ORBIS, and Morningstar. AQ was measured using discretionary accruals estimated via the Kasznik model. Correlation analysis tested associations between AQ and the selected performance indicators. Results show that firms involved in accounting manipulations had significantly lower AQ than their peers. However, no consistent correlations were found between AQ and EVA, dividends, stock prices, or volumes during the manipulation period. These findings suggest that the performance effects of manipulations are case-specific and shaped by additional factors, underscoring the importance of strong regulatory oversight and high-quality accounting practices. Ethically, our evidence underscores that misreporting corrodes investor trust and the public-interest mandate of financial reporting; accordingly, we stress the duties of boards, executives, auditors, and regulators to uphold faithful representation and timely disclosure, and to remediate misreporting when detected.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".