Rocky Brands (RCKY) Operating Expenses (2010 - 2026)
Rocky Brands (RCKY) reported Operating Expenses of $41.12 million for Q2 2026, up 13.8% from $36.13 million a year earlier but down 1.6% from the prior quarter.
Rocky Brands (RCKY) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Rocky Brands' Operating Expenses came in at $168.59 million, up 10.4% year-over-year; for FY2025, it came in at $160.1 million, up 8.2% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 15.6% (FY2020 to FY2025).
- By year, Operating Expenses came in at $147.94 million in FY2024 (+3.3%), $143.23 million in FY2023 (-20.9%), $181.18 million in FY2022 (+14.3%) and $158.56 million in FY2021 (+104.4%).
- Five-year quarterly Operating Expenses spans a low of $32.26 million in Q3 2023 and a high of $49.63 million in Q1 2022.
- Year over year, Operating Expenses has now increased in each of the last eight quarters, with growth averaging 10.5% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q3 2021 (growth of 119.1%); the low point was Q2 2023 (a decline of 26.5%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $41.8 million (Q1 2026), $48.14 million (Q4 2025) and $37.54 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 52.47 Bn | 18.46 Bn | 5.39 Bn | 4.08 Bn |
| 2 | Tapestry | 23.05 Bn | 19.01 Bn | 1.56 Bn | 1.12 Bn |
| 3 | Ralph Lauren | 21.35 Bn | 13.45 Bn | 1.44 Bn | 1.10 Bn |
| 4 | Deckers Outdoor | 11.12 Bn | 3.99 Bn | - | - |
| 5 | Lululemon Athletica | 10.25 Bn | 4.50 Bn | 1.46 Bn | 1.01 Bn |
| 6 | Levi Strauss | 7.60 Bn | 4.25 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 | 5.97 Bn | 4.27 Bn | 493.89 Mn | -37.80 Mn |
| 9 | Crocs | 5.64 Bn | 5.06 Bn | 700.71 Mn | 415.03 Mn |
| 10 | Rocky Brands | 334.63 Mn | 324.12 Mn | 60.80 Mn | 41.12 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 41.12 Mn |
| Mar 31, 2026 | 41.80 Mn |
| Dec 31, 2025 | 48.14 Mn |
| Sep 30, 2025 | 37.54 Mn |
| Jun 30, 2025 | 36.13 Mn |
| Mar 31, 2025 | 38.30 Mn |
| Dec 31, 2024 | 44.67 Mn |
| Sep 30, 2024 | 33.58 Mn |
| Jun 30, 2024 | 33.53 Mn |
| Mar 31, 2024 | 36.17 Mn |
| Dec 31, 2023 | 35.99 Mn |
| Sep 30, 2023 | 32.26 Mn |
| Jun 30, 2023 | 35.37 Mn |
| Mar 31, 2023 | 39.60 Mn |
| Dec 31, 2022 | 43.09 Mn |
| Sep 30, 2022 | 40.31 Mn |
| Jun 30, 2022 | 48.16 Mn |
| Mar 31, 2022 | 49.63 Mn |
| Dec 31, 2021 | 45.08 Mn |
| Sep 30, 2021 | 44.21 Mn |
Rocky Brands 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=RCKY&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "RCKY", "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=RCKY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();