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Record W4399559874 · doi:10.1080/1540496x.2024.2353105

Economic Policy Uncertainty and Syndication: Evidence from China’s Venture Capital Market

2024· article· en· W4399559874 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEmerging Markets Finance and Trade · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsAcadia University
FundersNational Natural Science Foundation of China
KeywordsWeb syndicationVenture capitalChinaSocial venture capitalBusinessFinancial systemCapital marketEconomicsFinanceMonetary economicsPolitical science

Abstract

fetched live from OpenAlex

This study investigates how economic policy uncertainty (EPU) impacts the syndication behaviors of venture capital firms (VCs). Analyzing data from China, we find that EPU boosts the likelihood that VCs take syndicated investments. When facing great EPU, compared with their domestic and private counterparts, foreign and state-backed VCs exhibit a greater propensity for syndication, particularly in collaboration with VCs from distinct capital backgrounds. Further, EPU reduces VCs’ risk-taking tendencies and exacerbates inadequate information exchange in the market, thereby making VCs more inclined to syndicate their investments. Finally, EPU adversely affects VCs’ exit performance, but syndication helps alleviate this impact.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.222 · 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