Oxford Industries (OXM) Operating Expenses (2010 - 2026)
Oxford Industries (OXM) recorded Operating Expenses of $229.47 million in fiscal Q2 2027 (quarter ended Aug 1, 2026), up 1.7% from $225.58 million a year earlier and up 1.0% from the prior quarter.
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Analysis
Oxford Industries (OXM) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Oxford Industries' Operating Expenses came in at $826.37 million as of Aug 1, 2026, up 2.6% year-over-year; for FY2026 (ended Jan 31, 2026), it came in at $944.8 million, up 10.5% from FY2025.
- Annual Operating Expenses has a five-year compound annual growth rate of 13.9% (FY2021 to FY2026).
- Across earlier fiscal years, Operating Expenses came in at $854.85 million in FY2025 (-8.5%), $934.32 million in FY2024 (+35.0%), $692 million in FY2023 (+20.6%) and $573.64 million in FY2022 (+16.4%).
- The fiscal Q2 2027 figure is the highest quarterly Operating Expenses in data going back to fiscal Q2 2011.
- On a year-over-year basis, Operating Expenses has increased for six consecutive quarters, with growth averaging 3.0% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 29.1% in fiscal Q1 2024, against a decline of 21.9% in fiscal Q4 2024 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $227.27 million (Q1 2027), $157.08 million (Q4 2026) and $212.55 million (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 53.14 Bn | 19.13 Bn | 5.39 Bn | 4.08 Bn |
| 2 | Tapestry | 22.87 Bn | 18.83 Bn | 1.56 Bn | 1.12 Bn |
| 3 | Ralph Lauren | 21.39 Bn | 13.49 Bn | 1.44 Bn | 1.10 Bn |
| 4 | Deckers Outdoor | 11.06 Bn | 3.93 Bn | - | - |
| 5 | Lululemon Athletica | 10.32 Bn | 4.58 Bn | 1.46 Bn | 1.01 Bn |
| 6 | Levi Strauss | 7.62 Bn | 4.28 Bn | 979.10 Mn | 856.90 Mn |
| 7 | Gildan Activewear | 6.55 Bn | 5.54 Bn | 459.76 Mn | 283.86 Mn |
| 8 | Birkenstock Holding | 6.07 Bn | 4.38 Bn | 493.89 Mn | -37.80 Mn |
| 9 | Crocs | 5.90 Bn | 5.31 Bn | 700.71 Mn | 415.03 Mn |
| 10 | Oxford Industries | 374.46 Mn | 339.97 Mn | 291.13 Mn | 229.47 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 1, 2026 | 229.47 Mn |
| May 2, 2026 | 227.27 Mn |
| Jan 31, 2026 | 157.08 Mn |
| Nov 1, 2025 | 212.55 Mn |
| Aug 2, 2025 | 225.58 Mn |
| May 3, 2025 | 222.71 Mn |
| Feb 1, 2025 | 152.30 Mn |
| Nov 2, 2024 | 204.72 Mn |
| Aug 3, 2024 | 216.85 Mn |
| May 4, 2024 | 213.10 Mn |
| Feb 3, 2024 | 153.44 Mn |
| Oct 28, 2023 | 194.82 Mn |
| Jul 29, 2023 | 205.23 Mn |
| Apr 29, 2023 | 203.15 Mn |
| Jan 28, 2023 | 196.43 Mn |
| Oct 29, 2022 | 175.03 Mn |
| Jul 30, 2022 | 163.14 Mn |
| Apr 30, 2022 | 157.41 Mn |
| Jan 29, 2022 | 152.64 Mn |
| Oct 30, 2021 | 137.51 Mn |
API Access
Oxford Industries Operating Expenses 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=operating-expenses&ticker=OXM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "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=operating-expenses&ticker=OXM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();