Bibliographic record
Abstract
As said in Proverbs(28:20) and Second Corinthians(9:6), stock investment, not speculation, can be promoted only if efficient risk management is put in place. This study examines the impact of oil price changes on stock price performances of top world consuming countries in order to provide investors in stock markets with an opportunity for global portfolio optimization. Monthly stock prices and oil prices for ten countries (USA, China, Japan, Russia, India, Brazil, Saudi Arabia, Germany and Korea) from July, 1997 to March, 2015 are modeled for EGARCH(1,1) estimation. The empirical results indicate that all stock markets except for Germany are positively correlated to the changes in oil prices with Russian and Canadian markets having particularly strong correlation with oil price changes, and there is only one Granger causal relationship to Indian stock market from oil price changes at 5 percent significance level. With respect to the spillover effect of returns and conditional variance by EGARCH(1,1) estimations, oil price changes impact stock market performances in all countries and the volatility spillover from oil prices is found in stock markets in USA, China, Japan, Brazil and Canada. The Christian stock investment can be supported by this study considering its contribution to the efficient investment risk management.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".