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Record W2071728628

Does the Response of Competitors to Privatization Announcements Reflect Competitive or Industry-Wide Information Effect? International Evidence

2006· article· en· W2071728628 on OpenAlexaff
Isaac Otchere

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsCompetitor analysisMonetary economicsShareholderGovernment (linguistics)PortfolioBusinessMarket economyEconomicsCorporate governanceFinance
DOInot available

Abstract

fetched live from OpenAlex

We examine the stock price reaction of rival firms to the announcement of the privatization of their industry counterparts to infer information about the intra-industry effects of privatization. We find that the rival firms reacted negatively to the privatization announcements, suggesting that the announcement effects reflect competitive rather than positive industry effects. The reaction is stronger for industry counterparts in low economic freedom countries than those in high economic freedom countries. Interestingly, we also find that full privatization announcements generate larger negative abnormal returns for rival firms than partial privatization announcements where the privatized firm gains only partial autonomy from the government. In this regard, we find that, as the proportion of government ownership reduces, subsequent partial privatization elicits stronger market reaction from rival firms. The negative abnormal returns earned by shareholders of rival firms are not due to price pressure and portfolio rebalancing effects resulting from index composition changes. We conclude that the negative effects documented for the rival firms reflect investors' concern about the potential competitive effects resulting from privatization of the state enterprise.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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