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Compare · FIG vs ADBE · 2026

Figma vs Adobe

A year of returns, risk, and volatility, compared.

Figma (FIG) and Adobe (ADBE) are compared across trailing return, volatility, drawdown, and risk-adjusted metrics.

Gale Finance Team
Written by Gale Finance Team
Sid Kalla
Reviewed by Sid Kalla CFA Charterholder

Returns shown in USD.

Quick answer

Which is a better investment: FIG or ADBE?

Over the past year, ADBE outperformed FIG. ADBE returned -24.0% compared with FIG’s -64.9%. ADBE had the better risk-adjusted return, with a Sharpe ratio of -0.61 versus FIG’s -0.95. ADBE was less volatile than FIG, and ADBE had a smaller max drawdown than FIG.

Total Return
FIG -64.9%
ADBE -24.0%
Sharpe Ratio
FIG -0.95
ADBE -0.61
Annualized Volatility
FIG 80.9%
ADBE 39.2%
Max Drawdown
FIG -78.2%
ADBE -47.4%

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

FIG Total Return
-64.9%
ADBE Total Return
-24.0%

Relative Performance of FIG vs ADBE (Normalized to 100)

FIG ADBE

Normalized to 100 at start date for comparison

Trade FIG or ADBE

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Affiliate disclosure

Key Takeaways

  • Total Return: FIG delivered a -64.9% total return, while ADBE returned -24.0% over the same period. ADBE outperformed on total returns.
  • Risk-Adjusted Return (Sharpe Ratio): Both Sharpe ratios were negative (ADBE -0.61 vs FIG -0.95), meaning both underperformed the risk-free rate; ADBE was less negative.
  • Volatility (Annualized): FIG was more volatile, with 80.9% annualized volatility, versus 39.2% for ADBE.
  • Maximum Drawdown: ADBE's maximum drawdown was -47.4%, while FIG experienced a deeper drawdown of -78.2%.
  • Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), FIG's VaR was -8.31% and its Expected Shortfall (CVaR) was -11.40%; ADBE's were -4.36% and -5.97%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
  • Skew & Kurtosis: Skew: FIG -0.06 vs ADBE -0.19. Excess kurtosis: FIG 1.27 vs ADBE 0.92. Negative skew leans downside; higher excess kurtosis means fatter tails.
  • Tail Days & Extremes: 2σ tail days (down/up): FIG 5/8, ADBE 9/7. Worst day: FIG -19.92% (2025-09-04) vs ADBE -7.58% (2026-03-13). Best day: FIG +16.83% (2025-10-08) vs ADBE +7.36% (2026-05-29).
  • Risk ratios: Sortino - FIG: -1.33 vs. ADBE: -0.83 , Calmar - FIG: -0.83 vs. ADBE: -0.51 , Sterling - FIG: -0.88 vs. ADBE: -0.59 , Treynor - FIG: -0.60 vs. ADBE: -0.83 , Ulcer Index - FIG: 60.24% vs. ADBE: 25.90%

Investment Comparison

If you invested $10,000 in each asset on August 22, 2025:

FIG $3,505.82 -64.9%
ADBE $7,603.08 -24.0%

Difference: $4,097.26 (ADBE ahead)

Figma vs Adobe Performance Over Time

Metric FIG ADBE
30 Days 26.2% 26.1%
90 Days 19.3% 12.5%
180 Days 3.9% 6.5%
1 Year -64.9% -24%

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

Figma vs Adobe Correlation

Average Correlation
moderately correlated
0.50
Current (30-day) 0.61
30-day rolling range -0.09 to +0.83

Figma and Adobe are moderately correlated over the past year. With a correlation of 0.50, 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) 0.61
Average (full period) 0.50
Minimum (30-day rolling) -0.09
Maximum (30-day rolling) 0.83

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
FIG
-78.2%
ADBE
-47.4%

Figma experienced its maximum drawdown of -78.2% from 2025-08-22 to 2026-06-25. It has not yet recovered to its previous peak.

Adobe experienced its maximum drawdown of -47.4% from 2025-09-18 to 2026-06-25. It has not yet recovered to its previous peak.

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

Figma vs Adobe Volatility (FIG vs ADBE)

FIG Volatility
80.9%
±5.1% 1-day vol
ADBE Volatility
39.2%
±2.47% 1-day vol
1-day volatility (1σ)
FIG
±5.1%
ADBE
±2.47%

Figma's 80.9% annualized volatility translates to about ±5.1% one-standard-deviation daily volatility.

Adobe's 39.2% annualized volatility translates to about ±2.47% one-standard-deviation daily volatility.

FIG had the wider volatility profile over this window. That means its day-to-day return distribution was broader; ADBE 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 FIG and ADBE

Sharpe Ratio: FIG vs. ADBE

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 Annualized volatility 0 100% vol 80.9% · excess -76.6% vol 39.2% · excess -23.7%
excess return / total volatility
Formula Sharpe=E[R]RfσR\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. Both Sharpe ratios were negative (ADBE -0.61 vs FIG -0.95), meaning both underperformed the risk-free rate; ADBE was less negative.

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 FIG and ADBE

Sortino Ratio: FIG vs. ADBE

Return per downside volatility

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

Higher is better
Frequency (days) Daily return (%) target -21.4% +18.3% 60 0
excess return / downside volatility
Formula Sortino=E[R]Rfσdown\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). ADBE 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: FIG 57.5% vs ADBE 28.8%. 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 FIG and ADBE

Calmar Ratio: FIG vs. ADBE

CAGR per worst drawdown

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

Higher is better
0% FIG -65.1% -78.2% ADBE -24.0% -47.4%
CAGR / max drawdown
Formula Calmar=CAGRMaxDD\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. ADBE 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 FIG and ADBE

Sterling Ratio: FIG vs. ADBE

Return per average drawdown

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

Higher is better
0% -21% -41% -62% -82% 10% drawdown threshold
excess annual return / average deep drawdown
Formula Sterling=CAGRRfD>10%\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%). ADBE 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 FIG and ADBE

Treynor Ratio: FIG vs. ADBE

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
Asset return Market return 0 0 β 1.27 β 0.29
excess return / market beta
Formula Treynor=E[R]Rfβ\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. FIG 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 FIG and ADBE

Ulcer Index: FIG vs. ADBE

Drawdown pain

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

Lower is better
0% -21% -41% -62% -82%
root-mean-square drawdown
Formula UI=E[Dt2]\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. ADBE 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): Figma vs. Adobe

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(PtPt1)\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: FIG vs. ADBE (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
FIG VaR 5% ES 5% ADBE VaR 5% ES 5% -25.6% 0% +25.6% Daily log return
VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.
Formula VaR5%=Q0.05(rt),ES5%=E[rtrtVaR5%]\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) FIG ADBE
5% VaR (daily log return) -8.31% -4.36%
5% Expected Shortfall (CVaR) -11.40% (worst 13 days) -5.97% (worst 13 days)
Skew -0.06 -0.19
Excess kurtosis 1.27 0.92
2σ tail days (down / up) 5 / 8 9 / 7
Worst day -19.92% (2025-09-04) -7.58% (2026-03-13)
Best day +16.83% (2025-10-08) +7.36% (2026-05-29)

Downside co-moves (2σ) — 1-Year

Computed on shared dates only (n=250). 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: FIG vs. ADBE (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 FIG and ADBE crossed their own 2σ downside threshold.

-2σ ADBE -2σ FIG Joint downside zone -9.0% 0% +9.0% +25.3% 0% -25.3% ADBE daily log return FIG daily log return
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 FIG and ADBE had a big down day (2σ)

Date (interval) FIG ADBE
2026-02-03 -10.88% -7.31%

Days when FIG had a big down day

Date (interval) FIG ADBE
2025-09-04 -19.92% -1.20%
2025-10-10 -10.13% -2.87%
2026-02-03 -10.88% -7.31%
2026-06-02 -10.44% -4.35%
2026-08-06 -14.85% +0.35%

Days when ADBE had a big down day

Date (interval) FIG ADBE
2025-10-29 -2.08% -6.13%
2026-01-13 -7.80% -5.41%
2026-02-03 -10.88% -7.31%
2026-03-13 -0.53% -7.58%
2026-04-23 -9.65% -6.63%
2026-06-11 -2.27% -6.25%
2026-06-12 -4.14% -6.76%
2026-06-17 +3.73% -5.33%
2026-07-30 -4.04% -5.90%

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 Figma vs. Adobe (1-Year)

Metric FIG ADBE
Total Return -64.9% -24.0%
Annualized Volatility 80.9% 39.2%
Sharpe Ratio -0.95 -0.61
Sortino Ratio -1.33 -0.83
Calmar Ratio -0.83 -0.51
Sterling Ratio -0.88 -0.59
Treynor Ratio -0.60 -0.83
Ulcer Index 60.24% 25.90%
Max Drawdown -78.2% -47.4%
Avg Correlation to S&P 500 0.25 0.15
5% VaR (daily log return) -8.31% -4.36%
5% Expected Shortfall (CVaR) -11.40% -5.97%
Skew -0.06 -0.19
Excess kurtosis 1.27 0.92
2σ tail days (down / up) 5 / 8 9 / 7
Audit this calculation

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

Inputs & conventions

Shared window for pair metrics
2025-08-22 → 2026-08-21 (last shared close).
Rolling correlation sample (shared closes)
221 rolling 30-day values (from 250 shared daily returns).
Annualization (days/year)
FIG: 252 days/year; ADBE: 252 days/year.
Risk-free rate
Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:
  • FIG: 3.81% over 2025-08-22 → 2026-08-21.
  • ADBE: 3.81% over 2025-08-22 → 2026-08-21.
Volatility drag (rule of thumb)
Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.
  • FIG: ≈ -32.7%/yr
  • ADBE: ≈ -7.7%/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
rt=PtPt11r_t = \frac{P_t}{P_{t-1}} - 1
σann=σ(rt)A\sigma_{ann} = \sigma(r_t)\sqrt{A}
drag12σann2\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2
S=Arˉrfσ(rt)AS = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}
So=ArˉrfE[min(0,rtrf/A)2]ASo = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}
MDD=mint(PtmaxstPs1)MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)
ρ=cov(rA,rB)σAσB\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}
t=ln(PtPt1)\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)
Notation
PtP_t
Price on day t.
rtr_t
Simple daily return.
t\ell_t
Log daily return.
rˉ\bar{r}
Average daily return.
σ(rt)\sigma(r_t)
Standard deviation of daily returns.
AA
Annualization factor (days/year).
rfr_f
Annual risk-free rate.

Figma vs Adobe: Frequently Asked Questions

Which has higher volatility: FIG or ADBE?

FIG showed higher volatility at 80.9% annualized, compared to 39.2% for ADBE Over the past year. Higher volatility means larger price swings in both directions.

Does FIG provide diversification when held with ADBE?

FIG and ADBE are moderately correlated over the past year, with an average correlation of 0.50. This offers some diversification benefit, though they still tend to move together during major market moves.

How bad are the worst 5% days for FIG vs ADBE?

Over the past year, FIG's 5% VaR was -8.31% and its 5% Expected Shortfall was -11.40% (worst 13 days). ADBE's were -4.36% and -5.97% (worst 13 days).

Do FIG and ADBE crash together on bad days?

On shared dates (n=250), when ADBE has a 2σ down day, FIG also does 11.1% (1/9 days). In the other direction, when FIG has one, ADBE also does 20.0% (1/5 days).

Which has better risk-adjusted returns: FIG or ADBE?

Both assets posted negative Sharpe ratios Over the past year (ADBE -0.61 vs FIG -0.95), meaning both underperformed the risk-free rate; ADBE was less negative.

Can FIG and ADBE be combined in a portfolio?

Yes, though allocation sizing matters. Their moderate correlation offers some diversification benefits. FIG's higher volatility (80.9%) means even small allocations can materially impact overall portfolio risk.

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