Digital Brands (DBGI) EV to EBITDA (2021 - 2026)
Digital Brands' (DBGI) quarterly EV to EBITDA came in at 0.13 in Q2 2026, up 707.17% on a YoY basis from 0.02 in Q2 2025, and down 79.12% quarter-over-quarter from 0.63 in Q1 2026.
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Digital Brands (DBGI) EV to EBITDA (2021 - 2026) Analysis & Trends
Digital Brands (DBGI) has reported EV to EBITDA for 6 consecutive years, with 0.13 the latest figure, recorded in Q2 2026.
- For the quarter ending Q2 2026, EV to EBITDA rose 707.17% year-over-year to 0.13; the trailing twelve-month figure through Jun 2026 stood at 0.02 (up 758.7% YoY), and the FY2025 full-year result was 0.11, up 616.81% from the prior year.
- EV to EBITDA slipped to 0.13 in Q2 2026 per DBGI's latest filing, from 0.63 in the prior quarter.
- Across five years, EV to EBITDA topped out at 3.55 in Q3 2025 and bottomed at 39.63 in Q1 2024.
- Historically, EV to EBITDA has averaged 3.67 across 5 years, with a median of 0.42 in 2023.
- The sharpest annual moves came in 2024 and 2025: EV to EBITDA plunged 1752.7% in 2024, then surged 1662.47% in 2025.
- Over 5 years, EV to EBITDA stood at 1.05 in 2022, then surged by 41.1% to 0.62 in 2023, then surged by 105.14% to 0.03 in 2024, then surged by 360.38% to 0.15 in 2025, then fell by 10.87% to 0.13 in 2026.
- According to Business Quant data, EV to EBITDA over the past three periods registered 0.13, 0.63, and 0.15 for Q2 2026, Q1 2026, and Q4 2025 respectively.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Lululemon Athletica | 10.45 Bn | 9.06 Bn | 1.46 Bn |
| 2 | Levi Strauss | 7.65 Bn | 6.67 Bn | 979.10 Mn |
| 3 | Gildan Activewear | 7.18 Bn | 6.91 Bn | 459.76 Mn |
| 4 | V F | 5.02 Bn | 4.35 Bn | 917.04 Mn |
| 5 | Kontoor Brands | 3.63 Bn | 3.57 Bn | 328.26 Mn |
| 6 | Pvh | 3.38 Bn | 2.42 Bn | 1.32 Bn |
| 7 | Columbia Sportswear | 2.91 Bn | 2.29 Bn | 358.43 Mn |
| 8 | Warby Parker | 2.78 Bn | 2.48 Bn | 136.46 Mn |
| 9 | Figs | 2.06 Bn | 1.76 Bn | 147.86 Mn |
| 10 | Digital Brands | 2.45 Mn | 1.06 Mn | 324,265.00 |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 0.13 |
| Mar 31, 2026 | 0.63 |
| Dec 31, 2025 | 0.15 |
| Sep 30, 2025 | 3.55 |
| Jun 30, 2025 | -0.02 |
| Mar 31, 2025 | 0.88 |
| Dec 31, 2024 | 0.03 |
| Sep 30, 2024 | -0.23 |
| Jun 30, 2024 | -1.30 |
| Mar 31, 2024 | -39.63 |
| Dec 31, 2023 | -0.62 |
| Sep 30, 2023 | -0.84 |
| Jun 30, 2023 | 0.62 |
| Mar 31, 2023 | -2.14 |
| Dec 31, 2022 | -1.05 |
| Sep 30, 2022 | -2.47 |
| Jun 30, 2022 | -1.15 |
| Mar 31, 2022 | -22.66 |
| Dec 31, 2021 | -14.96 |
| Sep 30, 2021 | -5.63 |
API Access
Digital Brands EV to EBITDA API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=ev-to-ebitda&ticker=DBGI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ev-to-ebitda", "ticker": "DBGI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=ev-to-ebitda&ticker=DBGI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();