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Record W3121161351 · doi:10.1287/mnsc.2013.1887

Do Temporary Increases in Information Asymmetry Affect the Cost of Equity?

2014· article· en· W3121161351 on OpenAlexaboutno aff
Shai Levi, Xiao‐Jun Zhang

Bibliographic record

VenueManagement Science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation asymmetryAdverse selectionMarket liquidityPortfolioEquity (law)Private information retrievalMonetary economicsEconomicsStock (firearms)Quarter (Canadian coin)Affect (linguistics)Factor analysis of information riskBusinessFinancial economicsFinanceRisk management information systemsInformation system

Abstract

fetched live from OpenAlex

Prior literature finds that long-lasting changes in firms' disclosure policies and information environment affect the cost of equity. Information asymmetry, however, also changes during the fiscal quarter. Firms disclose information periodically, and in between disclosure dates, traders can obtain private information, and adverse-selection risk increases. Such temporary increases in information asymmetry are usually considered to be diversifiable or too small to impact expected stock returns. In addition, investors may postpone trades or sell other assets in their portfolio on high information asymmetry days. We, however, find that returns increase significantly on days during the fiscal quarter when adverse-selection risk is high and liquidity low. Consistent with theory, we show that temporary asymmetry affects returns when investors demand liquidity and market makers bear risk for carrying capacity and providing it. This paper was accepted by Mary Barth, accounting.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.028
GPT teacher head0.245
Teacher spread0.217 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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