Weyco (WEYS) Operating Expenses (2010 - 2026)
Weyco's Operating Expenses came in at $26.76 million for Q2 2026, up 25.5% from $21.33 million a year earlier and up 18.6% from the prior quarter.
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Analysis
Weyco (WEYS) Operating Expenses (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Weyco reported Operating Expenses of $94.71 million, up 0.3% year-over-year; for FY2025, it was $90.06 million, down 5.1% from FY2024.
- Operating Expenses has declined in each of the last three years, though with a five-year compound annual growth rate of 0.9% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $94.91 million in FY2024 (-6.8%), $101.86 million in FY2023 (-2.1%), $104.03 million in FY2022 (+27.3%) and $81.75 million in FY2021 (-5.1%).
- The Q2 2026 figure represents the highest quarterly Operating Expenses since Q4 2024.
- Year-over-year, Operating Expenses increased in 1 of the last eight quarters, with an average decline of 1.0%.
- The fastest year-over-year change in Operating Expenses over five years came in Q4 2021 (growth of 42.3%), and the weakest in Q3 2021 (a decline of 25.6%).
- Business Quant data shows WEYS's Operating Expenses at $22.56 million (Q1 2026), $23.65 million (Q4 2025) and $21.73 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 53.94 Bn | 19.93 Bn | 5.39 Bn | 4.08 Bn |
| 2 | Tapestry | 22.74 Bn | 18.69 Bn | 1.56 Bn | 1.12 Bn |
| 3 | Ralph Lauren | 21.47 Bn | 13.57 Bn | 1.44 Bn | 1.10 Bn |
| 4 | Deckers Outdoor | 11.15 Bn | 4.02 Bn | - | - |
| 5 | Lululemon Athletica | 10.72 Bn | 4.97 Bn | 1.46 Bn | 1.01 Bn |
| 6 | Levi Strauss | 7.64 Bn | 4.29 Bn | 979.10 Mn | 856.90 Mn |
| 7 | Gildan Activewear | 6.52 Bn | 5.51 Bn | 459.76 Mn | 283.86 Mn |
| 8 | Birkenstock Holding | 6.06 Bn | 4.37 Bn | 493.89 Mn | -37.80 Mn |
| 9 | Crocs | 5.89 Bn | 5.30 Bn | 700.71 Mn | 415.03 Mn |
| 10 | Weyco | 450.23 Mn | 92.91 Mn | 43.78 Mn | 26.76 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 26.76 Mn |
| Mar 31, 2026 | 22.56 Mn |
| Dec 31, 2025 | 23.65 Mn |
| Sep 30, 2025 | 21.73 Mn |
| Jun 30, 2025 | 21.33 Mn |
| Mar 31, 2025 | 23.34 Mn |
| Dec 31, 2024 | 26.99 Mn |
| Sep 30, 2024 | 22.74 Mn |
| Jun 30, 2024 | 21.43 Mn |
| Mar 31, 2024 | 23.76 Mn |
| Dec 31, 2023 | 29.06 Mn |
| Sep 30, 2023 | 23.72 Mn |
| Jun 30, 2023 | 22.31 Mn |
| Mar 31, 2023 | 26.78 Mn |
| Dec 31, 2022 | 31.05 Mn |
| Sep 30, 2022 | 25.18 Mn |
| Jun 30, 2022 | 24.11 Mn |
| Mar 31, 2022 | 23.70 Mn |
| Dec 31, 2021 | 27.95 Mn |
| Sep 30, 2021 | 18.00 Mn |
API Access
Weyco 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=WEYS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WEYS", "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=WEYS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();