Earnings manipulation and cash holdings: a Beneish M-score analysis in G7 nations
Bibliographic record
Abstract
This study examines the relationship between earnings manipulation and cash holdings in non-financial firms across G7 countries from 2006 to 2022, using 111,640 firm-year observations from 9,766 listed companies. Earnings manipulators are identified using the Beneish M-Score. The analysis explores how manipulation relates to cash-holding practices across institutional settings. While prior studies mainly focused on single-country contexts, this study applies a unified detection approach in a cross-country setting, offering broader insights into how governance and culture influence corporate liquidity policies. Results show that manipulators hold significantly more cash than non-manipulators in the US, UK, Canada, France, and Italy, but not in Germany and Japan. This variation reflects firm-level factors such as overvaluation and financial distress, and country-level traits like ownership concentration, strength of accounting and auditing enforcement, and individualism. In France and Italy, precautionary cash accumulation is linked to moderate enforcement and concentrated ownership. In contrast, the US, UK, and Canada exhibit strong enforcement and individualistic cultures, encouraging cash hoarding to manage litigation and governance pressures. Overall, the results underscore the interplay between firm incentives and institutional environments in shaping fraudulent firms’ liquidity strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".