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Geopolitical Risk and the Cost of Capital

2020· article· en· W3045599577 on OpenAlexaff
Richard W. Carney, Omrane Guedhami, Sadok El Ghoul, Helen Wang

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeopoliticsCost of equityEmerging marketsCost of capitalEquity (law)Equity capital marketsBusinessEconomicsCapital (architecture)Capital marketPrivate equityMonetary economicsFinancial economicsFinanceMarket economyGeographyPolitical science

Abstract

fetched live from OpenAlex

One of the most fundamental determinants of firm competitiveness is the cost of capital. Existing research has focused on firm- and country-level factors, but largely overlooked a highly salient systemic factor – geopolitical risk. We posit that geopolitical events that elevate investor risk will prompt equity investors to shift their capital away from emerging markets to safer mature markets, raising the cost of equity capital for firms located in emerging economies. Based on a sample of 10,916 observations spanning 18 countries from 1990 to 2015, we find that higher geopolitical risk increases the cost of equity capital, on average. These results are robust to alternative measures for the cost of equity, to a variety of robustness tests, and to the inclusion of an extended range of control variables. We also find that this relationship is moderated by constraints on executive power, the strength of legal and regulatory institutions, a common law legal tradition, and cross-listing in the United States.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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