From oil spills to electric thrills: BYD’s rise and the market dynamics powering automaker stocks
Bibliographic record
Abstract
Purpose This study examines how oil market volatility and clean energy trends impact the stock performance of automakers, specifically comparing traditional manufacturers with electric vehicle (EV) producers such as BYD and Tesla. The objective is to assess the extent to which traditional automakers are sensitive to oil market fluctuations, while EV manufacturers align more closely with clean energy dynamics, particularly during global market crises. Design/methodology/approach Using daily data from January 2013 to December 2023, we conduct linear regressions, GARCH, DCC-GARCH and the Diebold–Yilmaz connectedness approaches in the analysis. We use these econometric models to capture volatility patterns, correlations and cross-market spillovers. Findings Traditional manufacturers are affected by both oil prices and clean energy development. While traditional automakers remain more vulnerable to oil price volatility, global leading EV manufacturers BYD and Tesla are less sensitive to oil price shocks and show strong alignment with clean energy indices. Significant volatility spillovers are observed during global crises, such as the COVID-19 pandemic and the Russia–Ukraine conflict. Originality/value The paper uniquely integrates clean energy indices into the analysis of oil price impacts on automaker stocks. By comparing traditional and EV manufacturers using advanced econometric models, it sheds light on the literature of energy markets and sustainable financial markets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".