The Influence of the Federal Reserve System Interest Rate Hike on Tesla: Empirical Research Based on ARIMA Model
Bibliographic record
Abstract
On 23 March 2022, the Federal Reserve System announced interest rate hikes and increased 25 basis points. After that, a series of negative monetary policies were carried out. Tesla CEO stated that the price of cars must be reduced when interest rates rose sharply. This means that Tesla's stock price will continue to decrease. The 25 -basis points raised the rate of interest rates and the highest interest rate reached 5 % to respond to inflation. The rate hikes of The Federal Reserve System have a great impact on the world. This article selected all stock data from Tesla from the listing to August this year (including daily data, weekly data, and monthly data) and uses ARIMA model to model and analyses the data. It will compare with the actual value of the Federal Reserve System. According to the study, the trend of the decline in stock prices after the RECERAL Reserve System raised interest rates is the same as the trend of research prediction in this article. Its interest rate hikes will cause Tesla's stock price to fall.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".