Uber Technologies vs Lyft (UBER vs LYFT) 2026: Stock Returns, Risk & Volatility

Q: Which is a better investment: UBER or LYFT?

Over the past year, LYFT looks like the better investment: it had higher total return (LYFT +14.9% vs UBER -3.9%) and a higher Sharpe ratio (LYFT 0.45 vs UBER -0.09). On risk, UBER was less volatile (UBER 32.5% vs LYFT 57.4%), UBER had a smaller max drawdown (UBER -30.9% vs LYFT -48.5%), and UBER had less severe losses on the worst 5% of days (Expected Shortfall of UBER -4.62% vs LYFT -7.16%).

Written by Gale Finance Team

Reviewed by Sid Kalla CFA Charterholder

Quick answer

Which is a better investment: UBER or LYFT?

Over the past year, LYFT outperformed UBER. LYFT returned +14.9% compared with UBER’s -3.9%. LYFT had the better risk-adjusted return, with a Sharpe ratio of 0.45 versus UBER’s -0.09. UBER was less volatile than LYFT, and UBER had a smaller max drawdown than LYFT.

Total Return

UBER -3.9%

LYFT +14.9%

Sharpe Ratio

UBER -0.09

LYFT 0.45

Annualized Volatility

UBER 32.5%

LYFT 57.4%

Max Drawdown

UBER -30.9%

LYFT -48.5%

Metric winners: Total Return: LYFT; Sharpe Ratio: LYFT; Annualized Volatility: UBER (less volatile); Max Drawdown: UBER (smaller drawdown).

UBER Total Return

↓ -3.9%

LYFT Total Return

↑ +14.9%

Relative Performance of UBER vs LYFT (Normalized to 100)

UBER LYFT

Normalized to 100 at start date for comparison

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Key Takeaways

Total Return: UBER delivered a -3.9% total return, while LYFT returned +14.9% over the same period. LYFT outperformed on total returns.
Risk-Adjusted Return (Sharpe Ratio): UBER had a negative Sharpe (-0.09) while LYFT was positive (0.45), indicating LYFT had meaningfully better risk-adjusted performance in this period.
Volatility (Annualized): LYFT was more volatile, with 57.4% annualized volatility, versus 32.5% for UBER.
Maximum Drawdown: UBER's maximum drawdown was -30.9%, while LYFT experienced a deeper drawdown of -48.5%.
Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), UBER's VaR was -3.55% and its Expected Shortfall (CVaR) was -4.62%; LYFT's were -4.85% and -7.16%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
Skew & Kurtosis: Skew: UBER 0.02 vs LYFT 0.97. Excess kurtosis: UBER 1.32 vs LYFT 11.80. Negative skew leans downside; higher excess kurtosis means fatter tails.
Tail Days & Extremes: 2σ tail days (down/up): UBER 7/6, LYFT 4/6. Worst day: UBER -6.89% (2025-11-20) vs LYFT -16.97% (2026-02-11). Best day: UBER +7.52% (2025-06-24) vs LYFT +28.08% (2025-05-09).
Risk ratios: Sortino - UBER: -0.13 vs. LYFT: 0.73 , Calmar - UBER: -0.13 vs. LYFT: 0.31 , Sterling - UBER: -0.46 vs. LYFT: 0.38 , Treynor - UBER: -0.03 vs. LYFT: 0.20 , Ulcer Index - UBER: 15.09% vs. LYFT: 23.94%

Investment Comparison

If you invested $10,000 in each asset on April 25, 2025:

UBER $9,607.72 -3.9%

LYFT $11,490.58 +14.9%

Difference: $1,882.86 (LYFT ahead)

Uber Technologies vs Lyft Performance Over Time

Metric	UBER	LYFT
30 Days	3.3%	6.6%
90 Days	-9.2%	-21.6%
180 Days	-20.6%	-31.5%
1 Year	-3.9%	14.9%

Shorter time frames can show different leaders as market conditions change. Consider your investment horizon when comparing performance.

Uber Technologies vs Lyft Correlation

Average Correlation

moderately correlated

0.51

Current (30-day) 0.72

30-day rolling range +0.01 to +0.87

Uber Technologies and Lyft are moderately correlated over the past year. With a correlation of 0.51, these assets show moderate co-movement, offering some diversification when held together.

For portfolio construction, this moderate correlation offers some diversification benefit, though the assets still tend to move together during major market moves.

Metric	Value
Current (30-day)	0.72
Average (full period)	0.51
Minimum (30-day rolling)	0.01
Maximum (30-day rolling)	0.87

Correlation measures how closely two assets move together. Values near +1 indicate strong co-movement, near 0 indicates independence, and negative values indicate inverse movement. Current, minimum, and maximum figures are 30-day rolling correlations on shared daily returns.

Drawdown

Maximum Drawdown

UBER

-30.9%

LYFT

-48.5%

Uber Technologies experienced its maximum drawdown of -30.9% from 2025-10-06 to 2026-03-27. It has not yet recovered to its previous peak.

Lyft experienced its maximum drawdown of -48.5% from 2025-11-12 to 2026-03-30. It has not yet recovered to its previous peak.

Smaller drawdowns and faster recoveries indicate lower downside risk and greater resilience during market stress.

Uber Technologies vs Lyft Volatility (UBER vs LYFT)

UBER Volatility

32.5%

±2.05% 1-day vol

LYFT Volatility

57.4%

±3.62% 1-day vol

1-day volatility (1σ)

UBER

±2.05%

LYFT

±3.62%

Uber Technologies's 32.5% annualized volatility translates to about ±2.05% one-standard-deviation daily volatility.

Lyft's 57.4% annualized volatility translates to about ±3.62% one-standard-deviation daily volatility.

LYFT had the wider volatility profile over this window. That means its day-to-day return distribution was broader; UBER was calmer, but lower volatility does not by itself mean better returns.

Treat the ± daily figure as a one-standard-deviation estimate from historical returns, not a forecast or expected absolute daily move. For context, 15-18% annualized volatility is roughly ±1% one-standard-deviation daily volatility.

Risk-adjusted ratios

Sharpe Ratio of UBER and LYFT

Sharpe Ratio: UBER vs. LYFT

Return per total volatility

Sharpe gives us excess return per unit of risk. Upside and downside volatility both count as risk.

Higher is better

excess return / total volatility

Formula

\displaystyle \mathrm{Sharpe} = \frac{\mathbb{E}[R] - R_f}{\sigma_R}

Sharpe ratio measures return per unit of risk (volatility). A higher Sharpe indicates better risk-adjusted performance. UBER had a negative Sharpe (-0.09) while LYFT was positive (0.45), indicating LYFT had meaningfully better risk-adjusted performance in this period.

A Sharpe above 1.0 is generally considered good, above 2.0 is excellent. Negative Sharpe means the asset underperformed the risk-free rate. Calculated on each asset's full 365-day lookback of available prices and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Sortino Ratio of UBER and LYFT

Sortino Ratio: UBER vs. LYFT

Return per downside volatility

Sortino keeps the return-over-risk idea, but only returns below the target rate count as volatility.

Higher is better

excess return / downside volatility

Formula

\displaystyle \mathrm{Sortino} = \frac{\mathbb{E}[R] - R_f}{\sigma_{\mathrm{down}}}

Sortino ratio measures return per unit of downside risk. Unlike Sharpe, it only counts downside deviation (returns below the target return). LYFT had better downside-adjusted returns.

A higher Sortino is better. It's useful when upside volatility is common (crypto is the obvious example). Downside deviation: UBER 22.8% vs LYFT 35.3%. Calculated on each asset's full 365-day lookback of available prices, using the daily risk-free rate as the target return, and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Calmar Ratio of UBER and LYFT

Calmar Ratio: UBER vs. LYFT

CAGR per worst drawdown

Calmar compares CAGR against the single deepest peak-to-trough loss over the period.

Higher is better

CAGR / max drawdown

Formula

\displaystyle \mathrm{Calmar} = \frac{\mathrm{CAGR}}{|\mathrm{MaxDD}|}

Calmar ratio compares CAGR to maximum drawdown. Higher Calmar means more return per unit of worst drawdown. LYFT posted the higher Calmar ratio.

Calmar is computed on each asset's full 365-day lookback and uses the max drawdown over that same window.

Sterling Ratio of UBER and LYFT

Sterling Ratio: UBER vs. LYFT

Return per average drawdown

Sterling smooths the drawdown penalty by using average drawdown events instead of only the worst one.

Higher is better

excess annual return / average deep drawdown

Formula

\displaystyle \mathrm{Sterling} = \frac{\mathrm{CAGR} - R_f}{\overline{D}_{>10\%}}

Sterling ratio measures excess return per unit of average drawdown (typically drawdowns worse than 10%). LYFT posted the higher Sterling ratio.

Sterling uses average drawdown events deeper than 10% and subtracts the risk-free rate to report excess return.

Treynor Ratio of UBER and LYFT

Treynor Ratio: UBER vs. LYFT

Excess return per market beta

Treynor divides excess annualized return by beta — the sensitivity of the asset to broad-market moves. The slope shown is each asset’s beta vs SPY.

Higher is better

excess return / market beta

Formula

\displaystyle \mathrm{Treynor} = \frac{\mathbb{E}[R] - R_f}{\beta}

Treynor ratio measures excess return per unit of market risk (beta) instead of total volatility. LYFT posted the higher Treynor ratio.

Treynor uses beta vs the S&P 500 (SPY) on shared dates and the average 3-month Treasury rate as the risk-free rate.

Ulcer Index of UBER and LYFT

Ulcer Index: UBER vs. LYFT

Drawdown pain

Ulcer Index is a risk index, not a return-over-risk ratio. Lower means smaller and shorter drawdowns.

Lower is better

root-mean-square drawdown

Formula

\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. UBER had the lower Ulcer Index (less drawdown pain).

Ulcer Index is computed from each asset's drawdown series over the full lookback window.

Tail Risk & Distribution Shape (1-Year): Uber Technologies vs. Lyft

This section looks at the shape of daily returns, not just the average. Tail stats are computed per asset on its own daily series (crypto includes weekends). We use daily log returns $\ln\left(\frac{P_t}{P_{t-1}}\right)$ so multi-day moves add cleanly.

Definitions: Value at Risk (VaR), Expected Shortfall, skew, kurtosis, and fat tails.

Tail Risk & Distribution Shape: UBER vs. LYFT (1-Year)

Actual daily return tails

The bars are real daily log-return observations from the article window. Darker bars are observations at or beyond each asset’s 5% VaR cutoff.

Observed returns

VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.

Formula

\displaystyle \mathrm{VaR}_{5\%}=Q_{0.05}(r_t),\quad \mathrm{ES}_{5\%}=\mathbb{E}[r_t\mid r_t\le \mathrm{VaR}_{5\%}]

Metric (1-Year)	UBER	LYFT
5% VaR (daily log return)	-3.55%	-4.85%
5% Expected Shortfall (CVaR)	-4.62% (worst 13 days)	-7.16% (worst 13 days)
Skew	0.02	0.97
Excess kurtosis	1.32	11.80
2σ tail days (down / up)	7 / 6	4 / 6
Worst day	-6.89% (2025-11-20)	-16.97% (2026-02-11)
Best day	+7.52% (2025-06-24)	+28.08% (2025-05-09)

Downside co-moves (2σ) — 1-Year

Computed on shared dates only (n=249). A “2σ downside move” means a shared-close log return more than 2 standard deviations below that asset’s own mean on this shared-date series. Dates below show simple returns (%) for readability.

Downside co-move map: UBER vs. LYFT (2σ)

Shared-close daily returns

Dots mark actual downside days: asset-colored dots are one-sided downside moves, and red dots are joint downside days. Grey dots add sampled shared-return context when available. The shaded lower-left zone shows where both UBER and LYFT crossed their own 2σ downside threshold.

2σ

Show downside tail dates

Dates below are shared-date observations. The “Date” is the period end (close). Tail thresholds are computed on log returns, but the table shows simple returns (%) for readability. Returns are computed from the previous shared close to this one (for example, Friday → Monday includes weekend moves).

Days when both UBER and LYFT had a big down day (2σ)

Date (interval)	UBER	LYFT
2025-11-04	-5.06%	-7.30%
2025-11-20	-6.89%	-6.84%

Days when UBER had a big down day

Date (interval)	UBER	LYFT
2025-05-29	-4.49%	-3.94%
2025-09-17	-4.99%	+13.13%
2025-11-04	-5.06%	-7.30%
2025-11-20	-6.89%	-6.84%
2025-12-10	-5.51%	-6.70%
2026-02-04	-5.15%	-3.58%
2026-02-20 → 2026-02-23	-4.25%	-4.73%

Days when LYFT had a big down day

Date (interval)	UBER	LYFT
2025-10-09	-3.30%	-6.81%
2025-11-04	-5.06%	-7.30%
2025-11-20	-6.89%	-6.84%
2026-02-11	-3.39%	-16.97%

Read this as “how ugly the ugly days get”, not as a precise forecast. One-year samples are small, so tail estimates are inherently noisy.

Full Comparison of Uber Technologies vs. Lyft (1-Year)

Metric	UBER	LYFT
Total Return	-3.9%	+14.9%
Annualized Volatility	32.5%	57.4%
Sharpe Ratio	-0.09	0.45
Sortino Ratio	-0.13	0.73
Calmar Ratio	-0.13	0.31
Sterling Ratio	-0.46	0.38
Treynor Ratio	-0.03	0.20
Ulcer Index	15.09%	23.94%
Max Drawdown	-30.9%	-48.5%
Avg Correlation to S&P 500	0.38	0.30
5% VaR (daily log return)	-3.55%	-4.85%
5% Expected Shortfall (CVaR)	-4.62%	-7.16%
Skew	0.02	0.97
Excess kurtosis	1.32	11.80
2σ tail days (down / up)	7 / 6	4 / 6

Audit this calculation

Formulas, inputs, and conventions used to compute the metrics on this page.

Inputs & conventions

Shared window for pair metrics: 2025-04-25 → 2026-04-23 (last shared close).
Rolling correlation sample (shared closes): 220 rolling 30-day values (from 249 shared daily returns).
Annualization (days/year): UBER: 252 days/year; LYFT: 252 days/year.
Risk-free rate: Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:

UBER: 4.17% over 2025-04-25 → 2026-04-23.

LYFT: 4.17% over 2025-04-25 → 2026-04-23.
Volatility drag (rule of thumb): Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.

UBER: ≈ -5.3%/yr

LYFT: ≈ -16.5%/yr
Data alignment: No forward fill. Correlation and tail co-moves are computed on shared closes only.
For cross-calendar pairs (e.g., crypto vs stocks), weekend/holiday moves roll into the next shared close.
Return conventions: Volatility/Sharpe/Sortino use simple daily returns. Tail-risk uses daily log returns for distribution stats (but tables show simple returns). Log returns.

Formulas

Daily simple return

r_t = \frac{P_t}{P_{t-1}} - 1

Volatility

\sigma_{ann} = \sigma(r_t)\sqrt{A}

Volatility drag (approx.)

\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2

Sharpe Ratio

S = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}

Sortino Ratio

So = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}

Maximum Drawdown

MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)

Average Correlation

\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}

Log returns

\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)

Notation

$P_t$: Price on day t.
$r_t$: Simple daily return.
$\ell_t$: Log daily return.
$\bar{r}$: Average daily return.
$\sigma(r_t)$: Standard deviation of daily returns.
$A$: Annualization factor (days/year).
$r_f$: Annual risk-free rate.

Uber Technologies vs Lyft: Frequently Asked Questions

Which has higher volatility: UBER or LYFT?

LYFT showed higher volatility at 57.4% annualized, compared to 32.5% for UBER Over the past year. Higher volatility means larger price swings in both directions.

Does UBER provide diversification when held with LYFT?

UBER and LYFT are moderately correlated over the past year, with an average correlation of 0.51. This offers some diversification benefit, though they still tend to move together during major market moves.

How bad are the worst 5% days for UBER vs LYFT?

Over the past year, UBER's 5% VaR was -3.55% and its 5% Expected Shortfall was -4.62% (worst 13 days). LYFT's were -4.85% and -7.16% (worst 13 days).

Do UBER and LYFT crash together on bad days?

On shared dates (n=249), when LYFT has a 2σ down day, UBER also does 50.0% (2/4 days). In the other direction, when UBER has one, LYFT also does 28.6% (2/7 days).

Which has better risk-adjusted returns: UBER or LYFT?

UBER had a negative Sharpe (-0.09) while LYFT was positive (0.45) Over the past year, indicating LYFT had meaningfully better risk-adjusted performance.

Can UBER and LYFT be combined in a portfolio?

Yes, though allocation sizing matters. Their moderate correlation offers some diversification benefits. LYFT's higher volatility (57.4%) means even small allocations can materially impact overall portfolio risk.

Explore our financial glossary

Which is a better investment: UBER or LYFT?

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Relative Performance of UBER vs LYFT (Normalized to 100)

Trade UBER or LYFT

Key Takeaways

Investment Comparison

Uber Technologies vs Lyft Performance Over Time

Uber Technologies vs Lyft Correlation

Drawdown

Uber Technologies vs Lyft Volatility (UBER vs LYFT)

Risk-adjusted ratios

Sharpe Ratio of UBER and LYFT

Sharpe Ratio: UBER vs. LYFT

Sortino Ratio of UBER and LYFT

Sortino Ratio: UBER vs. LYFT

Calmar Ratio of UBER and LYFT

Calmar Ratio: UBER vs. LYFT

Sterling Ratio of UBER and LYFT

Sterling Ratio: UBER vs. LYFT

Treynor Ratio of UBER and LYFT

Treynor Ratio: UBER vs. LYFT

Ulcer Index of UBER and LYFT

Ulcer Index: UBER vs. LYFT

Tail Risk & Distribution Shape (1-Year): Uber Technologies vs. Lyft

Tail Risk & Distribution Shape: UBER vs. LYFT (1-Year)

Downside co-moves (2σ) — 1-Year

Downside co-move map: UBER vs. LYFT (2σ)

Days when both UBER and LYFT had a big down day (2σ)

Days when UBER had a big down day

Days when LYFT had a big down day

Full Comparison of Uber Technologies vs. Lyft (1-Year)

Inputs & conventions

Formulas

Uber Technologies vs Lyft: Frequently Asked Questions

Which has higher volatility: UBER or LYFT?

Does UBER provide diversification when held with LYFT?

How bad are the worst 5% days for UBER vs LYFT?

Do UBER and LYFT crash together on bad days?

Which has better risk-adjusted returns: UBER or LYFT?

Can UBER and LYFT be combined in a portfolio?