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Record W4417291430 · doi:10.1108/jepp-11-2023-0115

Could an unconventional monetary policy have impact on firms' earnings management? The case of ECB's corporate sector purchase program

2025· article· en· W4417291430 on OpenAlexaboutno aff
Ανδρέας Ανδρικόπουλος, Michalis Bekiaris, Konstantinos Polyzos

Bibliographic record

VenueJournal of Entrepreneurship and Public Policy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsAsset (computer security)DebtSample (material)Quarter (Canadian coin)Quantitative easingCorporate debtCorporate financeCorporate bond

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.285
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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