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Record W4323786165 · doi:10.54691/bcpbm.v35i.3356

The Influence of the Ukraine Conflict on American Capital Market and Asset Price

2022· article· en· W4323786165 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

VenueBCP Business & Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFutures contractEconomicsVolatility (finance)Crude oilMonetary economicsFinancial economicsOil-storage tradeIndex (typography)Oil price

Abstract

fetched live from OpenAlex

The escalation of the Russian-Ukrainian war not only strengthened the geopolitical turmoil, but also affected the global capital market. This paper collects information about the S&P 500 and other indexes; Comparison of yields on 2-year, 5-year and 10-year U.S. treasury bonds and VIX index; CPI, gold price, gold inventory and gold futures price, etc.; Crude oil production, crude oil prices and inventories, crude oil futures prices and other data before and after the war (February 23, 2021 to February 23 and 2022, February 24, 2022 to now). It aims to study the objective impact of the Russian-Ukrainian war on the US capital market and asset price trend. It is found that the volatility of the stock index of U.S. stocks increased at the beginning of the war, and the overall U-shaped; The VIX index also increased, meaning investors wanted to increase purchases of assets with safe-haven properties such as U.S. Treasuries and gold; The short-term increase in demand for gold futures and crude oil futures will increase the price of gold and crude oil, but will gradually return to the normal level as the war progresses.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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