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Record W2973063262 · doi:10.5539/ijef.v11n10p1

Stock Price Synchronicity and Current and Potential Credit Ratings

2019· article· en· W2973063262 on OpenAlexvenueno aff
Bruno Figlioli, Rafael Moreira Antônio, Fabiano Guasti Lima

Bibliographic record

VenueInternational Journal of Economics and Finance · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsSynchronicityStock exchangeStock (firearms)BusinessMarket makerMonetary economicsRestricted stockEconomicsStock marketFinancial economicsFinance

Abstract

fetched live from OpenAlex

This study examines whether the stock prices reflet the relevant information on the companies´current and potential credit ratings. This investigation was carried out from the construct of stock price synchronicity, that is, the more the stock prices reflect the specific information of a certain company, the less the synchronicity of these prices in relation to the market general information tends to be. It would imply that the stock prices tend to be more informative on the companies´potential in generating future economic benefit and on their risk levels. For carrying out this study, information on the companies which have their shares listed at the Brazilian Stock Exchange (Brazil, Stock Exchange and Over-the-counter – B3) from 2010 to 2015 were analyzed. The results obtained point that the stock prices not only embody information on the alterations of the companies´s current credit ratings regarding the upgrade, but also reflect, with certain antecipation, the potential credit ratings. Nevertheless, the results indicate that not every credit rating class is associated with relevant information for the capital market.

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)0.017
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.275
Teacher spread0.253 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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