MétaCan
Menu
Back to cohort
Record W4412112769 · doi:10.1111/ecpo.70007

Anticorruption and Capital Market Pricing Efficiency: Evidence From China

2025· article· en· W4412112769 on OpenAlex
Yanyin Li, Yuan George Shan, Rong Xu, Xingmei Xu, Yusheng Xu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomics and Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsAlgoma University
Fundersnot available
KeywordsEconomicsChinaCapital marketCapital (architecture)Financial economicsMonetary economicsFinancePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT This study investigates the impact of anticorruption on capital market pricing efficiency through stock price synchronization. Using a data set from 3188 of China's A‐share listed firms with 20,673 firm‐year observations, our results show that anticorruption significantly reduces stock price synchronization. We conduct a series of robustness checks, including difference‐in‐differences analysis, informal monitoring mechanisms, alternative explanatory variables, and excluding observations during the Global Financial Crisis, and the conclusions remain consistent. The mechanism analysis reveals that anticorruption improves corporate disclosure quality at the micro level and the degree of marketization and government‐market relationship at the macro level, which reduces stock price synchronicity. This effect is more pronounced among state‐owned enterprises (SOEs), especially local SOEs. This study presents empirical evidence regarding the logic of development and governance dynamics in emerging economies.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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