Exploring the Correlation Between Crude Oil Prices and ETF Performance: A Predictive Analysis Using Crude Oil Index
Bibliographic record
Abstract
This paper investigates the feasibility of using crude oil prices to forecast the future performance of major Exchange-Traded Funds (ETFs) using data spanning from January 1, 2000, to June 1, 2024, which can have a significant impact on investment choices and portfolio management. The chosen ETFs include SPDR S&P 500 ETF (SPY), iShares MSCI Emerging Markets ETF (EEM), iShares MSCI Australia ETF (EWA), iShares MSCI Canada ETF (EWC), iShares China Large-Cap ETF (FXI), and Vanguard FTSE Europe ETF (VGK). The study comprises two main analyses: firstly, investigating the relationship between the Dow Jones Industrial Average Crude Oil Index and various ETFs; secondly, utilizing a predictive trading strategy with crude oil futures to predict ETF returns. The results indicate a notable inverse correlation between crude oil prices and ETF returns, suggesting that as crude oil prices increase, ETF returns tend to decrease. Moreover, the predictive strategy showcases significant annualized returns and favorable risk-adjusted performance, indicating that trading based on crude oil futures can lead to consistent profits. These findings offer valuable insights for both investors and policymakers, emphasizing the potential of crude oil prices as a predictive factor for ETF performance. By integrating crude oil price movements into their strategies, investors can improve their portfolio management and make more informed investment choices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".