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Record W2917968550

Leverage Choice and Credit Spreads when Managers Risk Shift

2016· article· en· W2917968550 on OpenAlexaboutno aff
Ulrich Hege, Rob Heinkel, Tim Johnson, Marcin Kacperczyk, Erwan Morellec, Hernán Ortiz‐Molina, Michael J. Roberts, Maryam Rastegar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsLeverage (statistics)IncentiveDebtStock (firearms)CashExecutive compensationBusinessPaymentCash flowCapital structureMonetary economicsEconomicsFinanceFinancial economicsActuarial scienceMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

We model the debt and asset risk choice of a manager with performance-insensitive pay (cash) and performance-sensitive pay (stock) to theoretically link compensation structure, leverage, and credit spreads. The model predicts that optimal leverage trades off the tax benefit of debt against the utility cost of ex-post asset substitution and that credit spreads are increasing in the ratio of cash-to-stock. Using a large cross section of U.S.-based corporate credit default swaps (CDS) covering 2001 to 2006, we find a positive association between cash-to-stock and CDS rates, and between cash to-stock and leverage ratios. CEO compensation typically includes both performance-sensitive and performance-insensitive components. This pay structure can be readily ratio nalized in a contracting setting (e.g., Holmstrom (1982)). Performance-sensitive payments link a manager's value-enhancing actions to his wealth, thereby aligning his incentives with those of the firm. These payments are risky, how ever, and in contracts with a risk-averse manager risk-sharing motives give rise to a role for the performance-insensitive component. Because compensa tion structure has a direct impact on manager objectives, it is natural to expect * Murray Carlson is from the Sauder School of Business at the University of British Columbia; Ali Lazrak is from the Sauder School of Business at the University of British Columbia and HEC Paris. We thank seminar participants at Carnegie Mellon University, Connor, Clark, and Lunn In vestment Management Ltd., HEC Lausanne, HEC Paris, Northwestern University, the Stockholm School of Economies, the University of British Columbia, the University of Calgary, the University of Colorado at Boulder, the University of Illinois at Urbana Champaign, the 2005 Northern Fi nance Association Meetings, the 2006 Society for Economic Dynamics, the 2007 American Finance Association Meetings, the 2008 French Finance Association Meetings, the 2009 European Finan cial Association Meetings, as well as two anonymous referees, the Associate Editor, Ulf Axelson, Haijoat Bhamra, Ivar Ekeland, Adlai Fisher, Thierry Foucault, Lorenzo Garlappi, Ron Gi ammarino, Robert Goldstein, Rick Green, Cam Harvey (the Editor), Ulrich Hege, Rob Heinkel, Tim Johnson, Marcin Kacperczyk, Erwan Morellec, Hernan Ortiz-Molina, Michael Roberts, Jean Charles Rochet, Dion Roseman, Neal Stoughton, Ilya Strebulaev (AFA discussant), Per Stromberg, Suresh Sundaresan, Michael Troege (FFA discussant), Fernando Zapatero, Josef Zechner (EFA discussant), Jaime Zender, and Song Zhongzhi for helpful comments. We are grateful to Markit Group Limited for providing us with credit default swap rate data and to Maryam Rastegar and Sandy Tanaka for their administrative help in acquiring these data. Financial support from the Social Sciences and Humanities Research Council of Canada (grant #410-2006-1345) is gratefully acknowledged. An Internet Appendix for this article is available online in the Sup plements and Datasets section, containing supplementary results and supporting material, at http ://www. afajof. org/supplements. asp.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.201
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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