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Record W4391849710 · doi:10.2308/horizons-2022-030

Does More Frequent Financial Reporting Bring the Future Forward?

2024· article· en· W4391849710 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

VenueAccounting Horizons · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEarningsBusinessAccountingVoluntary disclosureFinance

Abstract

fetched live from OpenAlex

SYNOPSIS Exploring mandatory financial reporting frequency changes in the United States from 1954 to 1972, we find that a mandatory increase in reporting frequency is associated with an increase in firms’ future earnings response coefficients. This effect is stronger for firms with higher sales seasonality or operating in industries with lower earnings persistence and for firms that provide more voluntary disclosures of forward-looking information after the reporting frequency increase. We also find that investors increase (decrease) the weight on long-term (near-term) earnings when pricing the firm after the reporting frequency increase. Our findings suggest that more frequent mandatory reporting can enhance the ability of investors to predict future earnings by providing additional useful information on future earnings and by triggering more voluntary disclosures. Our study informs the ongoing policy debates on mandatory financial reporting frequency by highlighting the informational benefit of frequent financial reporting for investors. Data Availability: Data are available from public sources identified in the paper. JEL Classifications: G14; M41; M48.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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