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Record W3124184358 · doi:10.1287/mnsc.2023.4710

The Effect of Managers on Systematic Risk

2023· article· en· W3124184358 on OpenAlexaboutno aff
Antoinette Schoar, Kelvin Yeung, Luo Zuo

Bibliographic record

VenueManagement Science · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDifferential effectsSystematic riskConservatismAccountingCorporate financeEconomicsRisk managementSystematic reviewBusinessRecessionFinanceManagementPolitical science

Abstract

fetched live from OpenAlex

Tracking the movement of top managers across firms, we document the importance of manager-specific fixed effects in explaining heterogeneity in firm exposures to systematic risk. In equilibrium, manager fixed effects on systematic risk are positively related with manager fixed effects on stock returns. These differences in systematic risk are partially explained by managers’ corporate strategies, such as their preferences for internal growth and financial conservatism. The early career experiences of managers starting their first job in a recession also contribute to differential loadings on systematic risk. These effects are more pronounced when managers wield more influence, as in smaller firms and firms that do not have an independent board. Overall, our results suggest that managers play an important role in shaping a firm’s systematic risk. This paper was accepted by Victoria Ivashina, finance. Funding: A. Schoar acknowledges financial support from the MIT Sloan School of Management. K. Yeung acknowledges financial support from City University of Hong Kong and the Cornell SC Johnson College of Business. L. Zuo acknowledges financial support from the Cornell SC Johnson College of Business and the University of Toronto Roger Martin Award for Emerging Leaders. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4710 .

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.208
Teacher spread0.201 · 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

Citations21
Published2023
Admission routes1
Has abstractyes

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