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The Influence of the Federal Reserve System Interest Rate Hike on Tesla: Empirical Research Based on ARIMA Model

2023· article· en· W4390270692 on OpenAlexaff
Yanjun Feng

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAutoregressive integrated moving averageInterest rateEconomicsStock (firearms)Inflation (cosmology)Federal Reserve Economic DataEconometricsMonetary economicsFinancial economicsMonetary policyTime seriesQuantitative easingCentral bankStatisticsGeographyMathematics

Abstract

fetched live from OpenAlex

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.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.105
GPT teacher head0.354
Teacher spread0.249 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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