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Record W4328095090 · doi:10.54691/bcpbm.v38i.3841

The Impact of the Increased Interest Rate on Nike's Stock Price Based on Stata

2023· article· en· W4328095090 on OpenAlexaff
Jiayu Zhang

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNikeEconomicsStock (firearms)Monetary economicsInterest rateFinancial economicsBondProfit (economics)Stock priceBusinessMicroeconomicsFinanceAdvertising

Abstract

fetched live from OpenAlex

In 2022, The Federal Reserve raised its benchmark interest rates three-quarters of a percentage point in its most aggressive hike since 1994, which gets US dollars more expensive to foreigners, and foreign currencies less valuable to the US. But in the meantime, Nike can’t rise its price in foreign countries immediately, thus with the same quantity of items being sold, their price decreases due to the exchange rate difference, and their profit diminishes. Since a stock’s value represent the ability to profit in the future, when Nike's ability to profit decreases, people tend to sell its stock and when the demand for Nike’s stock is smaller than that of supply, its price drops. In addition, as the interest rate increases, the mass is inclined to invest in bonds, and when bonds get more attractive people to invest less in stocks, thus as one of the listed corporations, owing to the demand for Nike’s stock price decrease, its stock price decreases. Whereas at the same time as the interest rate increase, more foreign capital is intended to invest in the US, thus the stock price of Nike might also increase. Thus whether the increased interest rate contributes to Nike's stock price is controversial. This paper mainly used a series of statistical models including VAR and ARMA-GARCH models etc. to see the net effect of interest rate on Nike's stock price, and then included the future study needed and limitations of this research.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.253
Teacher spread0.215 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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