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Record W2907471264 · doi:10.1111/1911-3846.12462

Déjà Vu: The Effect of Executives and Directors with Prior Banking Crisis Experience on Bank Outcomes around the Global Financial Crisis

2018· article· en· W2907471264 on OpenAlexvenueno aff
Anwer S. Ahmed, Brant E. Christensen, Adam J. Olson, Christopher G. Yust

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
FundersUniversity of OklahomaUniversity of CincinnatiTexas A and M UniversityWorld Bank Group
KeywordsFinancial crisisBusinessAccountingFinancial systemSalientQuality (philosophy)EconomicsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We investigate the effect of executives and directors with prior banking crisis experience on bank outcomes around the global financial crisis (GFC). Executives and directors with previous experience leading banks through a bank crisis may have been uniquely able to understand the risks, recognize the warnings signs early, and thus respond more effectively to the GFC. Controlling for other executive, director, and bank‐level characteristics, we examine whether bank performance, risk taking, and accounting quality in the period immediately before and during the GFC are affected by having executives or directors who previously served as bank executives or directors during the 1980s/1990s banking crisis (80s/90s crisis). Overall, we find that banks led by these crisis‐experienced executives and directors exhibit stronger performance, lower risk taking, and higher accounting quality in the period around the GFC. These effects are strongest among bank leaders for whom the 80s/90s crisis was most salient. Results are robust to propensity‐matched samples and other analyses performed to rule out alternative explanations. Our results suggest these individuals were able to learn from prior crisis experience.

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)0.001
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 score0.043
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.050
GPT teacher head0.312
Teacher spread0.261 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

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