Oxford Industries (OXM) Operating Leases (2019 - 2026)
Oxford Industries (OXM) posted Operating Leases of $391.14 million for fiscal Q2 2027 (quarter ended Aug 1, 2026), up 6.1% from $368.48 million a year earlier and up 2.0% from the prior quarter.
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Analysis
Oxford Industries (OXM) Operating Leases (2019 - 2026) Analysis & Trends
At the end of FY2026 (ended Jan 31, 2026), Oxford Industries' Operating Leases came in at $382.49 million, up 6.4% from FY2025.
- Annual Operating Leases has increased for four consecutive fiscal years, with a five-year compound annual growth rate of 9.8% (FY2021 to FY2026).
- In prior fiscal years, Oxford Industries' Operating Leases was $359.37 million in FY2025 (+47.5%), $243.7 million in FY2024 (+10.4%), $220.71 million in FY2023 (+10.6%) and $199.49 million in FY2022 (-16.9%).
- The fiscal Q2 2027 figure stands as the highest quarterly Operating Leases in data going back to fiscal Q1 2020.
- On a year-over-year basis, Operating Leases has increased in each of the last 16 quarters, with growth averaging 20.9% over the last eight quarters.
- The strongest year-over-year quarter for Operating Leases in the past five years was fiscal Q4 2025, with growth of 47.5%; the weakest was fiscal Q1 2023, with a decline of 18.1%.
- According to Business Quant data, Operating Leases for the three prior fiscal quarters was $383.44 million (Q1 2027), $382.49 million (Q4 2026) and $368.69 million (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Nike | 53.14 Bn | 19.13 Bn | 5.39 Bn |
| 2 | Tapestry | 22.87 Bn | 18.83 Bn | 1.56 Bn |
| 3 | Ralph Lauren | 21.39 Bn | 13.49 Bn | 1.44 Bn |
| 4 | Deckers Outdoor | 11.06 Bn | 3.93 Bn | - |
| 5 | Lululemon Athletica | 10.32 Bn | 4.58 Bn | 1.46 Bn |
| 6 | Levi Strauss | 7.62 Bn | 4.28 Bn | 979.10 Mn |
| 7 | Gildan Activewear | 6.55 Bn | 5.54 Bn | 459.76 Mn |
| 8 | Birkenstock Holding | 6.07 Bn | 4.38 Bn | 493.89 Mn |
| 9 | Crocs | 5.90 Bn | 5.31 Bn | 700.71 Mn |
| 10 | Oxford Industries | 374.46 Mn | 339.97 Mn | 291.13 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 1, 2026 | 391.14 Mn |
| May 2, 2026 | 383.44 Mn |
| Jan 31, 2026 | 382.49 Mn |
| Nov 1, 2025 | 368.69 Mn |
| Aug 2, 2025 | 368.48 Mn |
| May 3, 2025 | 360.94 Mn |
| Feb 1, 2025 | 359.37 Mn |
| Nov 2, 2024 | 310.39 Mn |
| Aug 3, 2024 | 298.70 Mn |
| May 4, 2024 | 296.08 Mn |
| Feb 3, 2024 | 243.70 Mn |
| Oct 28, 2023 | 226.24 Mn |
| Jul 29, 2023 | 219.21 Mn |
| Apr 29, 2023 | 223.17 Mn |
| Jan 28, 2023 | 220.71 Mn |
| Oct 29, 2022 | 225.92 Mn |
| Jul 30, 2022 | 180.09 Mn |
| Apr 30, 2022 | 185.37 Mn |
| Jan 29, 2022 | 199.49 Mn |
| Oct 30, 2021 | 206.48 Mn |
API Access
Oxford Industries Operating Leases 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-leases&ticker=OXM&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-leases", "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-leases&ticker=OXM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();