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Record W2971921673 · doi:10.1111/jfir.12199

EARNINGS CONFERENCE CALLS AND INSTITUTIONAL MONITORING: EVIDENCE FROM TEXTUAL ANALYSIS

2019· article· en· W2971921673 on OpenAlex

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

VenueThe Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsBank of Canada
Fundersnot available
KeywordsInstitutional investorEarningsTone (literature)BusinessAccountingMonetary economicsFinancial economicsEconomicsFinanceLinguisticsCorporate governance

Abstract

fetched live from OpenAlex

Abstract We document the effects of institutional investors on the qualitative information disclosure of firms during earnings conference calls. Using conference call and institutional ownership data between 2005 and 2016, we find that aggregate institutional ownership dampens conference call tone. The effects of institutional investors on tone are causal based on results from indexed firms. Consistent with hypotheses regarding investors' horizons, short‐term institutional investors are associated with a more positive conference call tone, as well as more opportunistic trading, whereas long‐term investors are associated with a more negative tone. Market participants can generally disentangle the impact of institutional investors on tone based on investor type.

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)0.004
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.062
GPT teacher head0.318
Teacher spread0.256 · 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