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Record W3111258796 · doi:10.1287/mnsc.2020.3737

Unfair “Fair Value” in Illiquid Markets: Information Spillover Effects in Times of Crisis

2020· article· en· W3111258796 on OpenAlexaff
Alex Dontoh, Fayez A. Elayan, Joshua Ronen, Tavy Ronen

Bibliographic record

VenueManagement Science · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBrock University
Fundersnot available
KeywordsSpillover effectCredit default swapFinancial crisisMonetary economicsValuation effectsStock (firearms)Equity (law)EconomicsMarket liquidityBook valueValuation (finance)BusinessFinancial economicsFinancial systemAccountingFinanceCredit risk

Abstract

fetched live from OpenAlex

We investigate the effects of write-downs on market prices and volumes under fair value accounting. We also examine the prominent role that illiquidity plays in exacerbating the direct and spillover effects of exit valuation on equity and credit default swap (CDS) markets. Using hand-collected data on write-down announcements made during and after the 2007–2009 financial crisis, we find that firms that wrote down assets in accordance with fair value rules experience significant abnormal negative stock returns and spikes in the CDS premiums written on their obligations; similar firms without write-downs exhibit sympathetic and significant negative abnormal returns and positive premiums. We find that both the direct effect of the write-downs and the indirect spillover effects resulting from crisis-related illiquidity in the markets for financial assets (affecting the magnitude of write-downs) and in the securities markets (affecting the reaction to the write-downs) during the financial crisis go beyond normal direct and information transfer effects and may have contributed to the adverse consequences of the crisis. This paper was accepted by Shiva Rajgopal, 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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