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Record W3123386884 · doi:10.1016/j.cjar.2015.08.001

Can media exposure improve stock price efficiency in China and why?

2015· article· en· W3123386884 on OpenAlexafffund
Jeong‐Bon Kim, Zhongbo Yu, Hao Zhang

Bibliographic record

VenueChina Journal of Accounting Research · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooFudan UniversityNational Natural Science Foundation of ChinaCity University of Hong Kong
KeywordsSynchronicityChinaStock (firearms)NewspaperStock priceStock marketMedia coverageCorporate governanceBusinessEconomicsFinancial economicsCommercializationMonetary economicsEconometricsFinanceAdvertisingMarketingGeography

Abstract

fetched live from OpenAlex

The media in China has undergone extensive commercialization to become more market-driven over the last 35 years. Based on a sample of over two million newspaper articles, this study investigates whether the media in China has an incremental impact on stock price efficiency. We find that: as media coverage of a firm increases, (1) its stock price synchronicity decreases; (2) the probability of informed trading of its stock increases; and (3) the extent to which its stock price deviates from random walk decreases. Our inter-regional analysis over thirty-one provinces/regions within China reveals that the effects of the media on decreasing stock price synchronicity, increasing the probability of informed trading, and reducing stock price deviation from random walk are stronger in regions of weaker institutional development. Our findings suggest that a market-driven media can play the role of compensating for the underdeveloped governance institutions in transitional economies such as China.

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)0.005
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.000
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 head0.055
GPT teacher head0.283
Teacher spread0.228 · 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

Citations30
Published2015
Admission routes2
Has abstractyes

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