The Impact of the Increased Interest Rate on Nike's Stock Price Based on Stata
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".