Resilience, Valuation, and Governance Interactions in Shaping Financial Accounting Manipulation: Evidence from Asia
Bibliographic record
Abstract
Financial accounting manipulation (FAM) remains a persistent concern in emerging Asian markets, yet existing studies typically assess firm resilience, market valuation, and institutional governance separately. This study addresses this gap by examining how the Resilience Factor (RF), Market Valuation (VAL), and Country Governance Index (CGI), along with their interaction effects, shape FAM. Using a panel dataset of 4303 non-financial firms across 17 Asian countries from 2012 to 2023 (51,636 observations), the analysis employs an Instrumental Variable–Two-Stage Least-Squares (IV-2SLS) approach to address endogeneity related to simultaneity and omitted variable bias. The results show that financially resilient firms are more prone to manipulation, market valuation reduces manipulation incentives, and stronger country governance constrains manipulation. Moreover, valuation moderates the governance–manipulation relationship, suggesting complementary monitoring roles between markets and institutions. Robustness checks across regions, industries, and the COVID-19 period confirm the findings. The study contributes to agency and institutional theory by highlighting how firm-level and country-level mechanisms jointly influence manipulation, offering policy implications for regulators and investors in Asian capital markets.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 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".