An Analysis of Tesla’s Stock Price Determinants Using VECM

초록

Purpose: This study investigates the key determinants of Tesla’s stock price by analyzing the effects of macroeconomic indicators, EV infrastructure, and firm performance, focusing on both long-run relationships and short-term dynamics. Design/methodology/approach: Using monthly data from July 2015 to March 2024 (105 observations), the study applies a Vector Error Correction Model (VECM) and Granger causality tests. Variables include GDP, M2, HPI, gasoline prices (GAS), EV charging locations (EVCL), and Tesla’s monthly sales (SALES). SHAP analysis on an XGBoost model complements the econometric findings. Findings: M2 has a significant positive long-run impact on Tesla’s stock price, while GAS shows a significant negative effect. GDP and HPI are not statistically significant. The VECM indicates a 25.9% monthly correction of disequilibrium. SHAP analysis highlights M2, lagged SALES, and GAS as key nonlinear predictors. Research limitations/implications: Results are limited to Tesla and a relatively small dataset. Causal inference is constrained by model choice. Future work should expand to other firms and adopt structural models. Originality/value: This study combines VECM with interpretable machine learning, offering a comprehensive view of stock price dynamics in the EV sector and practical insights for investors and policymakers.

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