Cross-market correlation patterns between oil prices, clean energy indices and automaker stocks (2013–2023) (IMAGE)
Caption
It presents the heatmap of return correlations among automaker stocks, oil price benchmarks and clean energy indices. The results reveal three key patterns that are directly linked to our hypotheses.
First, traditional automakers, Toyota, Honda, Ford and GM – exhibit strong and statistically
meaningful correlations with one another (ranging from 0.47 to 0.76), suggesting a high degree of co-movement driven by shared exposure to macroeconomic and industrial factors. This observation supports Hypothesis 1 (H1), which posits that traditional automakers are more sensitive to oil market dynamics and systemic volatility (Gong and Jia, 2024).
Second, EV manufacturers, notably Tesla and BYD, show weaker correlations with traditional automakers. For example, the Tesla–Ford correlation is 0.31, while BYD–GM is 0.27. These relatively low coefficients indicate a fundamental divergence in the drivers of EV stock performance, aligned with Hypothesis 2 (H2) regarding the limited sensitivity of EV manufacturers to oil price fluctuations (Kurkula et al., 2022).
Third, the influence of clean energy indices varies across EV manufacturers. Tesla exhibits a strong correlation with the NASDAQ Clean Edge Green Energy Index (CELS) at 0.63, suggesting a close alignment with the renewable energy sector. In contrast, BYD’s correlation with CELS is more moderate at 0.42. This should not be interpreted as a lack of clean energy orientation; rather, it reflects BYD’s distinctive positioning as a vertically integrated company with localized battery development and secure upstream material access, which may reduce its exposure to external market dependencies (Huang et al., 2023; Zi, 2023). These contrasting correlation patterns lend support to Hypothesis 3 (H3), which posits that the stock performance of EV manufacturers is increasingly shaped by clean energy dynamics rather than traditional oil market fluctuations (Baur and Todorova, 2018).
Credit
Yi Fang (Jilin University, China) Chengbo Fu and Soleiman Hashemishahraki (University of Northern British Columbia, Canada)
Usage Restrictions
This Original Content image is restricted for Non - Commercial Use Only and must include proper attribution to the creator.
License
Original content