Gorman Rupp (GRC) Operating Expenses (2009 - 2026)
Gorman Rupp (GRC) recorded Operating Expenses of $27.54 million in Q3 2026, up 6.5% from $25.86 million a year earlier and up 1.6% from the prior quarter.
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Analysis
Gorman Rupp (GRC) Operating Expenses (2009 - 2026) Analysis & Trends
On a TTM basis, Gorman Rupp's Operating Expenses came in at $105.88 million as of Sep 30, 2026, up 3.8% year-over-year; for FY2025, it was $116.72 million, up 0.6% from FY2024.
- Annual Operating Expenses has increased for six straight years, with a five-year compound annual growth rate of 12.7% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $116.01 million in FY2024 (+3.1%), $112.56 million in FY2023 (+16.7%), $96.42 million in FY2022 (+43.5%) and $67.2 million in FY2021 (+4.8%).
- The Q3 2026 figure is the highest quarterly Operating Expenses in data going back to Q2 2010.
- On a year-over-year basis, Operating Expenses has increased for three consecutive quarters, with growth averaging 2.2% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 73.7% in Q2 2022, against a decline of 3.8% in Q4 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $27.12 million (Q2 2026), $26.8 million (Q1 2026) and $24.41 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Caterpillar | 267.46 Bn | 238.77 Bn | 7.35 Bn | 16.47 Bn |
| 2 | Amphenol | 197.37 Bn | 192.21 Bn | 4.02 Bn | 7.11 Bn |
| 3 | Deere | 185.36 Bn | 194.13 Bn | 4.66 Bn | 10.73 Bn |
| 4 | Eaton | 137.64 Bn | 134.62 Bn | 3.11 Bn | 1.52 Bn |
| 5 | Parker-Hannifin | 121.80 Bn | 119.79 Bn | 2.26 Bn | 1.07 Bn |
| 6 | 3M | 97.84 Bn | 80.25 Bn | 2.78 Bn | 5.03 Bn |
| 7 | Emerson Electric | 93.37 Bn | 85.86 Bn | 2.74 Bn | 1.39 Bn |
| 8 | Illinois Tool Works | 81.86 Bn | 78.53 Bn | 1.91 Bn | 717.00 Mn |
| 9 | Vertiv Holdings | 74.77 Bn | 63.62 Bn | 1.64 Bn | 557.10 Mn |
| 10 | Gorman Rupp | 1.85 Bn | 1.69 Bn | 64.52 Mn | 27.54 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 27.54 Mn |
| Jun 30, 2026 | 27.12 Mn |
| Mar 31, 2026 | 26.80 Mn |
| Dec 31, 2025 | 24.41 Mn |
| Sep 30, 2025 | 25.86 Mn |
| Jun 30, 2025 | 26.04 Mn |
| Mar 31, 2025 | 25.11 Mn |
| Dec 31, 2024 | 25.01 Mn |
| Sep 30, 2024 | 25.68 Mn |
| Jun 30, 2024 | 24.93 Mn |
| Mar 31, 2024 | 24.89 Mn |
| Dec 31, 2023 | 26.00 Mn |
| Sep 30, 2023 | 23.23 Mn |
| Jun 30, 2023 | 24.19 Mn |
| Mar 31, 2023 | 23.24 Mn |
| Dec 31, 2022 | 20.99 Mn |
| Sep 30, 2022 | 22.08 Mn |
| Jun 30, 2022 | 24.11 Mn |
| Mar 31, 2022 | 15.88 Mn |
| Dec 31, 2021 | 13.94 Mn |
API Access
Gorman Rupp 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=GRC&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "GRC", "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=GRC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();