Does the New European Banking Regulation discourage Earnings Management?
Bibliographic record
Abstract
In the recent past, the financial crisis has shown important lacks in the EU regulation relating to the banking sector, making the introduction of a unified regulatory framework necessary. Since June 2009 the European Council has recommended a “Single Rulebook”, that is a unique and harmonizing discipline applicable to all financial institutions in the Single Market, become effective on January 2014. This prudential discipline requires much more minimum capital, liquidity and information transparency and it defines format and minimum standards of contents.The aim of this research is to investigate the relation between the new mandatory disclosure and earnings management policies in banking sector realized through Loan Loss Provisions (LLP), the component of income statement mainly subject to manipulations, especially in form of earnings smoothing. Because the new integrated regulatory framework requires a more transparent disclosure, we expected that accruals manipulation (basically LLP) could be discouraged. The empirical analysis is based on a sample of 116 listed European banks over the period prior (2011-2012-2013) and after (2014-2015-2016) the effective date of the Single Rulebook. The evidence confirm our hypothesis suggesting that this banking reform discourages earnings manipulation and improves earnings quality, making financial reporting more useful for investors. The results are important to the regulatory institutions (such as European Union and European Central Bank) supporting more stringent discipline introduced by Basel III.
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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 | 0.007 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".