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Record W3194535821 · doi:10.3390/jrfm14080372

Oil Market Factors as a Source of Commonality in Liquidity in International Equity Markets

2021· article· en· W3194535821 on OpenAlexvenueno aff
Abdulrahman Alhassan, Atsuyuki Naka, Abdullah M. Noman

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
FundersDeanship of Scientific Research, King Saud University
KeywordsMarket liquidityEquity (law)Volatility (finance)Monetary economicsEconomicsLiquidity riskFinancial economicsBusiness

Abstract

fetched live from OpenAlex

When stock markets are less liquid or illiquid, investors are expected to require compensation for taking the risk of not being able to sell quickly. Many studies have documented the existence of the co-movements (commonality) of market liquidity in equity markets as a priced factor. The primary objective of this paper is to introduce the oil market as a potential source of commonality in liquidity. We hypothesize that conditions specific to the oil market can contribute to commonality in liquidity affecting both supply-side and demand-side factors because of its importance to the global economy in general. To this aim, a sample of firms is drawn from 50 countries spanning the period from January 1995 to December 2015. We examine two channels that transmit the effect of oil market movements to the liquidity commonality in international equity markets, namely, oil price returns and oil price volatility. Seemingly unrelated regressions (SUR) are utilized to estimate the effect of oil factors on commonality in liquidity. We find that the returns and volatility of oil prices explain the commonality in liquidity in countries with higher integration with oil markets. In addition, we show that the effect of oil volatility is more pronounced for net oil exporters as opposed to net oil importers after controlling for oil sensitivity. These results are robust to controlling for possible sources of commonality in liquidity as found in the literature and alternative estimation specifications.

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)0.010
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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