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Record W3124500495 · doi:10.1111/1911-3846.12417

Do Corporate Site Visits Impact Stock Prices?

2018· article· en· W3124500495 on OpenAlexvenueno aff
Qiang Cheng, Fei Du, Brian Yutao Wang, Xin Wang

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessStock (firearms)Stock priceStock marketFinanceAccountingGeography

Abstract

fetched live from OpenAlex

ABSTRACT We examine the stock price impact of corporate site visits using a unique data set of site visits to listed firms in China. Our main findings are as follows. First, the market reaction around corporate site visits is statistically and economically significant and is stronger for group visits, visits conducted by mutual fund managers, visits covering accounting and finance topics, visits to firms with poor information environments, and visits to manufacturing firms. Second, the stock returns around site visits are positively associated with firms’ future performance. Third, the changes in visiting funds’ holdings are more predictive of firms’ future performance than those of nonvisiting funds. Overall, this study contributes to the literature by providing evidence that site visits are important venues for investors to collect information about firms and make informed trades.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.173
GPT teacher head0.343
Teacher spread0.170 · 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; both teacher heads agree on what is shown here.

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

Citations289
Published2018
Admission routes1
Has abstractyes

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