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Record W3215204626 · doi:10.33423/jabe.v22i10.3709

Corporate Sponsorship of the 2008, 2012, and 2016 Summer Olympics: A Test of Market Efficiency

2020· article· en· W3215204626 on OpenAlexvenueno aff
Frank W. Bacon, Joshua A. Hutchinson

Bibliographic record

VenueJournal of Applied Business and Economics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderEvent studyStock (firearms)Abnormal returnBeijingBusinessStock marketInvestment strategyEconomicsOrder (exchange)Tender offerFinancial economicsCorporate governanceAdvertisingFinanceStock exchange

Abstract

fetched live from OpenAlex

Corporate sponsorship is a form of advertising that companies pay to be associated with certain events. Firms with corporate sponsorships of an event such as the Olympic Games can maximize stockholder wealth by evaluating the returns on these investments. In order to evaluate the returns on these investment decisions, this study employs an event study methodology in the finance literature. Using the risk-adjusted event study methodology, this study tests the hypothesis that the risk-adjusted rates of return on the sponsor companies’ stock prices are significantly positively affected by this type of information. The event study tested the effect of the 2008 Beijing, 2012 London and 2016 Rio Summer Olympic games on the sponsor company’s stock prices. The opening ceremonies took place on August 8th, 2008, July 27th, 2012 and August 5th, 2016. Results for all 3 summer Olympic games and the combined global sample show positive gains to their risk-adjusted rates of return of stock prices leading to the opening ceremony, with small gains following the opening ceremony. The evidence also supports the semi-strong form of market efficiency. No investor was able to make an above normal return by acting on the event.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.132
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.233
Teacher spread0.193 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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