Oxford Industries (OXM) EBITDA (2010 - 2026)
Oxford Industries' EBITDA was $86.01 million in fiscal Q2 2027 (quarter ended Aug 1, 2026), up 104.8% from $42 million a year earlier and up 122.0% from the prior quarter.
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Analysis
Oxford Industries (OXM) EBITDA (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Oxford Industries' EBITDA was $64.21 million through Aug 1, 2026, down 55.4% year-over-year; for FY2026 (ended Jan 31, 2026), it was $34.62 million, down 81.5% from FY2025.
- In earlier fiscal years, EBITDA was $186.91 million in FY2025 (+17.0%), $159.79 million in FY2024 (-41.7%), $273.91 million in FY2023 (+31.7%) and $207.98 million in FY2022.
- The fiscal Q2 2027 figure marks the highest quarterly EBITDA since fiscal Q2 2024.
- Compared with a year earlier, EBITDA was higher in 1 of the last six quarters, with an average decline of 22.2%.
- The best year-over-year quarter for EBITDA over five years was fiscal Q4 2022 (growth of 377.1%); the worst was fiscal Q4 2026 (a decline of 78.0%).
- Per Business Quant data, OXM's EBITDA in the three fiscal quarters before Q2 2027 was $38.74 million (Q1 2027), $8.35 million (Q4 2026) and -$68.9 million (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | EBITDA (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 53.14 Bn | 19.13 Bn | 5.39 Bn | 1.50 Bn |
| 2 | Tapestry | 22.87 Bn | 18.83 Bn | 1.56 Bn | 500.90 Mn |
| 3 | Ralph Lauren | 21.39 Bn | 13.49 Bn | 1.44 Bn | 401.30 Mn |
| 4 | Deckers Outdoor | 11.06 Bn | 3.93 Bn | - | - |
| 5 | Lululemon Athletica | 10.32 Bn | 4.58 Bn | 1.46 Bn | 595.99 Mn |
| 6 | Levi Strauss | 7.62 Bn | 4.28 Bn | 979.10 Mn | 179.40 Mn |
| 7 | Gildan Activewear | 6.55 Bn | 5.54 Bn | 459.76 Mn | 244.22 Mn |
| 8 | Birkenstock Holding | 6.07 Bn | 4.38 Bn | 493.89 Mn | 244.59 Mn |
| 9 | Crocs | 5.90 Bn | 5.31 Bn | 700.71 Mn | 305.72 Mn |
| 10 | Oxford Industries | 374.46 Mn | 339.97 Mn | 291.13 Mn | 86.01 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 1, 2026 | 86.01 Mn |
| May 2, 2026 | 38.74 Mn |
| Jan 31, 2026 | 8.35 Mn |
| Nov 1, 2025 | -68.90 Mn |
| Aug 2, 2025 | 42.00 Mn |
| May 3, 2025 | 53.17 Mn |
| Feb 1, 2025 | 37.89 Mn |
| Nov 2, 2024 | 10.97 Mn |
| Aug 3, 2024 | 69.06 Mn |
| May 4, 2024 | 71.95 Mn |
| Feb 3, 2024 | -60.13 Mn |
| Oct 28, 2023 | 34.15 Mn |
| Jul 29, 2023 | 86.63 Mn |
| Apr 29, 2023 | 99.13 Mn |
| Jan 28, 2023 | 59.48 Mn |
| Oct 29, 2022 | 41.82 Mn |
| Jul 30, 2022 | 86.22 Mn |
| Apr 30, 2022 | 86.40 Mn |
| Jan 29, 2022 | 44.57 Mn |
| Oct 30, 2021 | 40.70 Mn |
API Access
Oxford Industries 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=ebitda&ticker=OXM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ebitda", "ticker": "OXM", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=ebitda&ticker=OXM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();