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Record W4388079785 · doi:10.1111/1911-3846.12915

Common institutional ownership and stock price crash risk

2023· article· en· W4388079785 on OpenAlexvenueno aff
Shenglan Chen, Hui Ma, Qiang Wu, Hao Zhang

Bibliographic record

VenueContemporary Accounting Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersHigher Education Discipline Innovation ProjectMinistry of Education, IndiaHong Kong Polytechnic UniversityNational Office for Philosophy and Social SciencesNational Natural Science Foundation of China
KeywordsInstitutional investorCorporate governanceIncentiveBusinessHoarding (animal behavior)ShareholderCrashStock (firearms)ExternalityCommon stockStock priceMonetary economicsFinanceEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract This paper presents new evidence on the economic benefits arising from common institutional ownership. We find a negative and significant effect of common institutional ownership on stock price crash risk. This effect is robust to a battery of robustness checks and is causal according to some identification tests, including difference‐in‐differences analyses on financial institution mergers. We find evidence that the negative effect is attributable to the monitoring role of common institutional owners—a role that is enabled by common owners' lower information processing cost and greater monitoring incentives owing to governance externalities. We also find that common owners negatively influence crash risk through constraining bad news hoarding and that common owners are more likely to force CEO turnover when a firm has higher crash risk. Overall, our results suggest that common institutional shareholders play a unique and effective monitoring role that fends off stock price crashes.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.111
GPT teacher head0.318
Teacher spread0.206 · 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.

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

Citations83
Published2023
Admission routes1
Has abstractyes

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