General Motors vs Tesla: 10-Year Scorecard
2016 - 2025
Cite This Scorecard
"General Motors vs Tesla: 10-Year Performance Scorecard (2016-2025)." Gale Finance, December 31, 2025. https://www.gale.finance/scorecard/gm-vs-tsla/ Free to use with attribution. Please link back to this page or gale.finance.
The Verdict
Year-by-Year Performance
Over 10 years, TSLA won 6 individual years while GM won 3. There were 1 ties.
| Year | General Motors | Tesla | Winner |
|---|---|---|---|
| 2016 | +9.7% | -4.4% | GM |
| 2017 | +21.4% | +43.5% | TSLA |
| 2018 | -16.7% | +3.8% | TSLA |
| 2019 | +13.4% | +34.9% | TSLA |
| 2020 | +12.8% | +720.1% | TSLA |
| 2021 | +44.7% | +44.8% | Tie |
| 2022 | -44.8% | -69.2% | GM |
| 2023 | +7.3% | +129.9% | TSLA |
| 2024 | +49.3% | +62.6% | TSLA |
| 2025 | +59.9% | +18.6% | GM |
| Total Wins | 3 wins | 6 wins | TSLA |
Cumulative Performance
This chart shows how $100 invested at the start of 2016 would have grown over time.
Price Comparison
Normalized to 100 at start date for comparison
Risk-Adjusted Metrics
How did each asset perform relative to the risk taken? Higher Sharpe, Sortino, and Calmar ratios indicate better risk-adjusted returns.
Tail-risk definitions: Value at Risk (VaR), Expected Shortfall, skew, kurtosis, and fat tails.
| Metric | GM | TSLA |
|---|---|---|
| Total Return | +203.5% | +2919.5% |
| CAGR | +11.8% | +40.6% |
| Volatility (Ann.) | +36.6% | +59.3% |
| Sharpe Ratio | 0.37 | 0.80 |
| Sortino Ratio | 0.54 | 1.21 |
| Calmar Ratio | 0.20 | 0.55 |
| Max Drawdown | -59.9% | -73.6% |
Sharpe and Sortino ratios use the period-average risk-free rate based on the 3-month U.S. Treasury yield (FRED: DGS3MO). To reproduce: take the simple average of daily DGS3MO values from 2016-01-01 to 2025-12-31; in this window the average is 4.23%.
Tail Risk & Distribution Shape
Tail-risk metrics summarize how each asset behaved on extreme days from 2016 to 2025. We compute them from daily log returns ( ).
VaR and Expected Shortfall (ES/CVaR) are historical (non-parametric) 1-day metrics and are not annualized.
| Metric | GM | TSLA |
|---|---|---|
| Return observations (n) | 2513 | 2513 |
| 5% VaR (daily) | -3.57% | -5.62% |
| 5% Expected Shortfall (daily) | -5.20% (avg of worst 126 days) | -8.58% (avg of worst 126 days) |
| 1% VaR (daily) | -5.73% | -9.76% |
| 1% Expected Shortfall (daily) | -8.45% (avg of worst 26 days) | -13.85% (avg of worst 26 days) |
| Skew | -0.08 | -0.06 |
| Excess kurtosis | 7.40 | 4.20 |
| 2σ tail days (down/up) | 65 / 68 | 68 / 64 |
| |z| > 3σ days (observed vs expected) | 38 (6.78) | 41 (6.78) |
| Worst day (simple return) | -17.32% (2020-03-18) | -21.06% (2020-09-08) |
| Best day (simple return) | +19.94% (2020-03-24) | +22.69% (2025-04-09) |
Downside Co-moves (Tail Dependence)
Using shared closes, a “tail day” is when each asset is in its own worst 5% (or 1%) of days in 2016–2025.
Show co-crash dates
These are shared-date intervals when both assets were in their own worst-tail days (shown as simple returns). Most recent 30 dates are shown.
Both in worst 5% days
| Date | GM | TSLA |
|---|---|---|
| 2018-02-08 | -3.87% | -8.63% |
| 2019-05-22 | -4.26% | -6.02% |
| 2019-08-14 | -4.67% | -6.54% |
| 2020-02-21 → 2020-02-24 | -4.50% | -7.46% |
| 2020-02-27 | -4.03% | -12.81% |
| 2020-03-06 → 2020-03-09 | -13.94% | -13.57% |
| 2020-03-12 | -11.38% | -11.62% |
| 2020-03-13 → 2020-03-16 | -15.01% | -18.58% |
| 2020-03-18 | -17.32% | -16.03% |
| 2020-04-01 | -7.31% | -8.10% |
| 2020-04-02 | -5.56% | -5.63% |
| 2020-04-21 | -5.09% | -7.99% |
| 2020-05-01 | -6.24% | -10.30% |
| 2020-09-03 | -4.78% | -9.02% |
| 2021-02-25 | -4.35% | -8.06% |
| 2022-04-26 | -4.47% | -12.18% |
| 2022-05-11 | -3.70% | -8.25% |
| 2022-05-18 | -5.96% | -6.80% |
| 2022-06-10 → 2022-06-13 | -7.80% | -7.10% |
| 2022-06-16 | -8.07% | -8.54% |
| 2022-07-08 → 2022-07-11 | -4.46% | -6.55% |
| 2022-09-29 | -5.65% | -6.81% |
| 2022-12-22 | -6.60% | -8.88% |
| 2023-01-27 → 2023-01-30 | -4.37% | -6.32% |
| 2024-12-31 → 2025-01-02 | -3.57% | -6.08% |
| 2025-04-03 | -4.34% | -5.47% |
| 2025-04-04 | -3.75% | -10.42% |
| 2025-04-10 | -4.39% | -7.27% |
Both in worst 1% days
| Date | GM | TSLA |
|---|---|---|
| 2020-03-06 → 2020-03-09 | -13.94% | -13.57% |
| 2020-03-12 | -11.38% | -11.62% |
| 2020-03-13 → 2020-03-16 | -15.01% | -18.58% |
| 2020-03-18 | -17.32% | -16.03% |
| 2020-05-01 | -6.24% | -10.30% |
Best and Worst Years
GM Best Year
GM Worst Year
TSLA Best Year
TSLA Worst Year
Maximum Drawdown
Maximum drawdown measures the largest peak-to-trough decline. Lower (less negative) is better.
Recovery time measures calendar days from the drawdown low back to the prior peak.
Correlation Analysis
The 10-year average correlation between General Motors and Tesla was 0.27. This low correlation suggests diversification benefits when holding both assets.
General Motors vs. Tesla Yearly Average Correlation (10-year)
Frequently Asked Questions
Which performed better over 10 years: General Motors or Tesla?
General Motors returned +203.5% compared to Tesla's +2919.5% from 2016 to 2025. Tesla delivered the higher total return. Tesla won 6 out of 10 individual years.
What would $10,000 invested in General Motors be worth today?
$10,000 invested in General Motors at the start of 2016 would be worth $30,349.29 by the end of 2025. The same amount in Tesla would be worth $301,947.09.
Which asset had better risk-adjusted returns?
Tesla had the higher Sharpe ratio (0.80 vs 0.37), indicating better risk-adjusted performance than General Motors.
How bad were the worst 5% days for General Motors vs Tesla?
From 2016 to 2025, GM had a 5% Expected Shortfall of -5.20% and a 5% VaR of -3.57%. TSLA's were -8.58% and -5.62%.
Do General Motors and Tesla crash together on bad days?
When TSLA was in its worst 5% days, GM was also in its worst 5% days 22.2% of the time (28 of 126). The reverse was 22.2% (28 of 126).
Methodology
- Price data sourced from Tiingo (GM) and Tiingo (TSLA)
- Volatility calculated as annualized standard deviation of daily returns
- Sharpe and Sortino ratios use the average 3-month Treasury rate as the risk-free rate
- Calmar ratio = CAGR / Maximum Drawdown
- Year-by-year returns calculated from first to last trading day of each calendar year
- Tail-risk metrics (VaR/ES, skew/kurtosis) use daily log returns in 2016–2025. Downside co-moves use shared closes (weekend/holiday moves roll into the next close).
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Disclaimer: This scorecard is for informational and educational purposes only and does not constitute investment, financial, legal, or tax advice. Past performance is not indicative of future results. All investments involve risk, including the possible loss of principal. Data sourced from third parties may contain errors or be delayed. Always conduct your own research and consult a qualified financial advisor before making investment decisions.