RELATIONSHIP BETWEEN OIL PRICE AND STOCK MARKETS BEFORE AND AFTER 2014-2015 OIL PRICE COLLAPSE
Bibliographic record
Abstract
The priming of this thesis investigates the price formation of oil and stock market shares which is followed by the introduction of previous studies. The evidence of the relationship between oil price and stock markets in previous papers are introduced globally and mainly from 21-century. Majority of the previous results indicates that oil price shock affects negatively to stock markets in most of the countries with the exception of oil producer countries or companies examined. However, contrary results with none significant effect occurred also. Reason for the asymmetric results can be that the economy consists of many factors impacting the relationship of oil price and stock markets. These factors are for example changes in wages, interest rates, commodity prices, stock market behavior or changes in technology or even in political situation. Many impacting factors or changes in different commodity prices may offset the changes in energy cost which complex the effect of oil price to the stock markets. \n \nParticularly, this research concentrates on the effect of oil price to stock markets on 2010s separately before and after the 2014 oil price collapse. The thesis investigates whether the relationship between oil and stock market is similar in the 2010s as in the previous literature and whether the price collapse has had any impact on the relationship. \nThe analysis is executed by conducting simpler two independent variable market models and multiple control variable models separately from time before the oil price collapse in 2014-2015 and after it. The research concentrates on important economic and oil regions including United States, Canada, Europe, Norway, China and Russia. \n \nThe results of the research are somewhat in line with the previous literature stating that countries with relatively large oil production industry often tend to have positive relationship between oil price and stock markets. The positive impact was slightly milder after concluding the control variables to the model in effort to make model more reliable. The oil price impact to the stock markets also seemed to be weaker after the oil price collapse, stating that the oil price might be less crucial in lower price levels, exception being Norway. This research does not find any significant negative relationship on oil price and stock markets in any of the regions in 2010s. When examining large economies, the oil price impact on stock markets seemed not to be significant, excluding China´s positive relationship before the oil price collapse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".