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Record W3013830156 · doi:10.1177/0148558x20934235

The Differential Informativeness of Positive and Negative Stock Returns

2020· article· en· W3013830156 on OpenAlexaboutno aff
Eli Amir, Shai Levi, Roy Zuckerman

Bibliographic record

VenueJournal of Accounting Auditing & Finance · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNegative informationStock (firearms)ConservatismQuarter (Canadian coin)Differential (mechanical device)Positive feedbackPositive relationshipInformation leakageMonetary economicsBusinessNegative feedbackDifferential effectsEconomicsPsychologySocial psychologyMedicineLawInternal medicinePolitical scienceStatistics

Abstract

fetched live from OpenAlex

We show negative stock returns reverse more and contain less information on the long-term changes in share prices than positive stock returns mostly on nondisclosure days, and these information differences between negative and positive returns decrease substantially on disclosure days. The results suggest investors are more likely to acquire positive information on nondisclosure days and to obtain both negative and positive information on disclosure days. Accounting conservatism and litigation exposure compels managers to reveal their negative information in disclosures, and if managers withhold negative information, they do it when investors are less likely to find the information on nondisclosure days. Moreover, we use the exogenous imposition of Regulation Fair Disclosure (Reg. FD) to demonstrate that positive information leakage from firms during the quarter is driving the positive slant in investors’ information. Taken together, our results suggest that disclosure plays an important role in the differential informativeness and reversals of positive and negative returns.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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 designOther design
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

Citations0
Published2020
Admission routes1
Has abstractyes

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