Weis Markets (WMK) Operating Expenses (2010 - 2026)
Weis Markets (WMK) posted Operating Expenses of $321.61 million for Q2 2026, up 7.8% from $298.32 million a year earlier and up 1.6% from the prior quarter.
Weis Markets (WMK) Operating Expenses (2010 - 2026) Analysis & Trends
For the trailing twelve months through Jun 27, 2026, Operating Expenses at Weis Markets was $1.26 billion, up 6.5% year-over-year; for FY2025, it came in at $1.21 billion, up 4.8% from FY2024.
- Annual Operating Expenses has increased for 13 consecutive years, with a five-year compound annual growth rate of 5.3% (FY2020 to FY2025).
- In prior years, Weis Markets' Operating Expenses was $1.16 billion in FY2024 (+2.8%), $1.13 billion in FY2023 (+0.2%), $1.12 billion in FY2022 (+16.1%) and $969 million in FY2021 (+3.4%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses in data going back to Q2 2010.
- On a year-over-year basis, Operating Expenses has increased in each of the last ten quarters, with growth averaging 6.3% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q4 2022, with growth of 15.5%; the weakest was Q4 2023, with a decline of 4.5%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $316.65 million (Q1 2026), $308.85 million (Q4 2025) and $308.28 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Walmart | 940.08 Bn | 896.52 Bn | 49.13 Bn | 39.75 Bn |
| 2 | Costco Wholesale | 441.76 Bn | 365.04 Bn | 12.19 Bn | 8.39 Bn |
| 3 | Sysco | 39.58 Bn | 33.83 Bn | 4.13 Bn | 3.15 Bn |
| 4 | Kroger | 38.03 Bn | 24.18 Bn | 7.86 Bn | 198.00 Mn |
| 5 | Dollar General | 29.55 Bn | 24.23 Bn | 3.68 Bn | 2.91 Bn |
| 6 | Caseys General Stores | 25.47 Bn | 23.47 Bn | 1.24 Bn | 754.11 Mn |
| 7 | Dollar Tree | 23.68 Bn | 20.30 Bn | 2.10 Bn | 1.43 Bn |
| 8 | US Foods Holding | 22.36 Bn | 22.15 Bn | 1.92 Bn | 1.48 Bn |
| 9 | Tractor Supply | 17.17 Bn | 16.34 Bn | 1.68 Bn | 1.02 Bn |
| 10 | Weis Markets | 1.85 Bn | 1.05 Bn | 328.40 Mn | 321.61 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 321.61 Mn |
| Mar 28, 2026 | 316.65 Mn |
| Dec 27, 2025 | 308.85 Mn |
| Sep 27, 2025 | 308.28 Mn |
| Jun 28, 2025 | 298.32 Mn |
| Mar 29, 2025 | 298.85 Mn |
| Dec 28, 2024 | 294.60 Mn |
| Sep 28, 2024 | 286.65 Mn |
| Jun 29, 2024 | 287.69 Mn |
| Mar 30, 2024 | 289.30 Mn |
| Dec 30, 2023 | 279.73 Mn |
| Sep 30, 2023 | 256.05 Mn |
| Jul 1, 2023 | 253.42 Mn |
| Apr 1, 2023 | 253.17 Mn |
| Dec 31, 2022 | 292.84 Mn |
| Sep 24, 2022 | 253.99 Mn |
| Jun 25, 2022 | 243.81 Mn |
| Mar 26, 2022 | 252.27 Mn |
| Dec 25, 2021 | 253.64 Mn |
| Sep 25, 2021 | 244.16 Mn |
Weis Markets 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=WMK&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WMK", "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=WMK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();