Could an unconventional monetary policy have impact on firms' earnings management? The case of ECB's corporate sector purchase program
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of the European Central Bank's Corporate Sector Purchase Program (CSPP) on the earnings-management practices of firms that issued eligible debt after 10 March 2016 and those that were finally targeted, under the Program. Design/methodology/approach The sample consists of 139 firms that issued CSPP-eligible debt, from 2013Q1, one year after the end of the European sovereign debt crisis, to 2019Q4, the quarter prior to the outbreak of the COVID-19 pandemic. We adopt the modified-Jones model as our baseline model to estimate the discretionary accruals. Findings Upward earnings management of firms that issued eligible debt, as well as of those whose securities were targeted, is constrained after the announcement of the Corporate Sector Purchase Program (March 10, 2016), especially in the quarters when asset purchases' volume was larger. Moreover, we provide some evidence that this tendency is more pronounced in firms with ultimate parents residing and listed in the euro area. We attribute these results to the easing of financing conditions, the reduction of the cost of capital and the boost of liquidity. Originality/value This paper is unique in examining the effects of corporate Quantitative Easing on 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".