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Record W2053642119 · doi:10.5539/ijef.v3n2p89

The Impact of Mergers and Acquisitions on the Performance of the Greek Banking Sector: An Event Study Approach

2011· article· en· W2053642119 on OpenAlexvenueno aff
Panagiotis Liargovas, Spyridon Repousis

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderMergers and acquisitionsEvent studyStock exchangeBusinessStock (firearms)Monetary economicsFinancial systemAccountingEconomicsFinanceCorporate governance

Abstract

fetched live from OpenAlex

This paper examines the impact of Greek mergers and acquisitions on the performance of the Greek Banking Sector during the period 1996-2009. With the use of event study methodology, we reject the “semi-strong form” of Efficient Market Hypothesis (EMH) of the Athens Stock Exchange. We find that ten days prior to the announcement of a merger and acquisition, shareholders receive considerable and significant positive cumulative average abnormal returns (CAARs). Also the results show that significant positive CAARs are gained upon the announcement of horizontal and diversifying bank deals. The overall results indicate that bank mergers and acquisitions have no impact and do not create wealth. We also examine operating performance of the Greek Banking Sector by estimating twenty financial ratios. Findings show that operating performance does not improve, following mergers and acquisitions. There are also controversial results when comparing merged to non-merged banks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.224
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations69
Published2011
Admission routes1
Has abstractyes

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