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Record W2887935625 · doi:10.1111/1911-3846.12405

Business Ties and Information Advantage: Evidence from Mutual Fund Trading

2018· article· en· W2887935625 on OpenAlexafffundvenue
Ying Duan, Edith Hotchkiss, Yawen Jiao

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMutual fundBusinessPortfolioPensionEarningsClosed-end fundFund of fundsPredictabilityStock (firearms)FinanceOpen-end fundPredictive powerInstitutional investorMonetary economicsEconomicsCorporate governance

Abstract

fetched live from OpenAlex

ABSTRACT This article examines whether ties to portfolio firms’ management via pension business relationships provide mutual funds with an informational advantage. Funds become related to portfolio companies when fund families serve as trustees for firms’ employee pension plans. Selling by related funds is more likely to be motivated by an information advantage than their buying, because the latter is heavily influenced by the desire to secure pension inflows. We find that stocks with larger net sales by related funds experience lower future returns. Information appears related to firm fundamentals, as the return predictability of related funds’ selling concentrates in stocks with negative future earnings surprises. Consistent with an information‐based explanation, the predictive power of related funds’ selling for future returns is more pronounced when information uncertainty about the stock is higher. Our results contribute to a growing literature that shows the sources of informed trading by institutions.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.266
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.007
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 head0.163
GPT teacher head0.322
Teacher spread0.159 · 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.

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

Citations27
Published2018
Admission routes3
Has abstractyes

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