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Record W4386712639 · doi:10.1111/corg.12556

Chief executive officer private firm experience and idiosyncratic risk

2023· article· en· W4386712639 on OpenAlexaff
Dev R. Mishra

Bibliographic record

VenueCorporate Governance An International Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Saskatchewan
FundersAsian Finance AssociationUniversity of Connecticut
KeywordsCorporate governancePrincipal–agent problemBusinessSystematic riskAgency (philosophy)Upper echelonsCorporate financeEarningsPolitical riskAgency costAccountingFinancePoliticsPublic relationsMarketingShareholderStrategic managementPolitical science

Abstract

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Abstract Research Question/Issue We examine chief executive officers' (CEOs) lifetime work experience in private firms and its potential influence in shaping managers' style in public firms and their corporate policies and thus the market's perception of a firm's risk. Research Findings/Insights We find that the idiosyncratic risk of public firms increases with the extent of CEO work experience in privately owned firms ( CEO private experience ). While there is no evidence of higher investment risk taking by private CEOs, the proportion of private‐firm work experience has a positive association with disclosure deficiency, decrease in manager‐owner agency conflicts, and an increase in political risk revelations at earnings conference calls, which, in turn, are associated with the elevation of idiosyncratic risk. Theoretical/Academic Implications The findings of this study underscore arguments in the upper echelons theory, imprinting theory, and behavioral agency theory. The study also has implications for literature related to corporate disclosure, governance, and political risk. Practitioner/Policy Implications Idiosyncratic risk is important for firms, as the literature suggests it hurts a firm's ability to finance future capital investments; therefore, it is optimal for corporate boards to have strategies in place to monitor and offer orientation packages targeted at alleviating CEO style heterogeneities presented by their prior work experience in private firms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.267
Teacher spread0.230 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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