Bank competition and earnings management: Does institutional quality matter?
Bibliographic record
Abstract
Purpose This paper aims to investigate the relationship between bank competition and earnings management and the effect of institutional quality on this relationship. Design/methodology/approach To investigate the main objective of this research, we use a sample of 114 European Union (EU) banks from 2006 to 2019. The quantile regression method is the estimation technique used. Findings The results indicate that opportunistic earnings management increases in a highly competitive market. However, in countries with high institutional quality, competition can act as an external governance mechanism. Practical implications This paper has important implications for both researchers and financial authorities in the EU. Regulators should give more importance to the impact of the degree of competition on earnings management and reform the regulatory environment. Indeed, they should put in place new, stronger laws; better ensure their enforcement and aim for a level of competition in the market, which will reduce banking opacity and the opportunistic behavior of bank managers. Originality/value Most of the studies investigate bank-specific characteristics as determinants of earnings management, while few focus on country- and market-level factors. This research addresses this gap by empirically examining how institutional quality moderates the relationship between bank competition and earnings management.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".