Smith & Wesson Brands (SWBI) Operating Expenses (2010 - 2026)
Smith & Wesson Brands' Operating Expenses was $28.05 million in fiscal Q1 2027 (quarter ended Jul 31, 2026), up 12.1% from $25.03 million a year earlier but down 11.6% from the prior quarter.
Smith & Wesson Brands (SWBI) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Smith & Wesson Brands' Operating Expenses was $114.91 million through Jul 31, 2026, up 12.0% year-over-year; for FY2026 (ended Apr 30, 2026), it came in at $111.89 million, up 8.3% from FY2025.
- Operating Expenses shows a five-year compound annual growth rate of -2.9% (FY2021 to FY2026).
- In earlier fiscal years, Operating Expenses was $103.3 million in FY2025 (-6.9%), $110.99 million in FY2024 (+4.6%), $106.13 million in FY2023 (-13.7%) and $122.91 million in FY2022 (-5.0%).
- Quarterly Operating Expenses has moved between $23.2 million (fiscal Q3 2025) and $36.6 million (fiscal Q2 2022) over five years.
- Compared with a year earlier, Operating Expenses has increased for three straight quarters, with growth averaging 2.6% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was fiscal Q3 2026 (growth of 24.5%); the worst was fiscal Q2 2023 (a decline of 27.0%).
- Per Business Quant data, SWBI's Operating Expenses in the three fiscal quarters before Q1 2027 was $31.75 million (Q4 2026), $28.88 million (Q3 2026) and $26.23 million (Q2 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amer Sports | 15.40 Bn | 11.45 Bn | 1.07 Bn | -909.20 Mn |
| 2 | Hasbro | 12.62 Bn | 8.41 Bn | 867.20 Mn | 887.10 Mn |
| 3 | Acushnet Holdings | 4.89 Bn | 4.64 Bn | 445.83 Mn | 267.00 Mn |
| 4 | Mattel | 3.81 Bn | 486.56 Mn | 542.12 Mn | - |
| 5 | YETI Holdings | 3.16 Bn | 2.48 Bn | 322.52 Mn | 229.01 Mn |
| 6 | Callaway Golf | 2.55 Bn | 8.09 Mn | 306.70 Mn | 191.90 Mn |
| 7 | Peloton Interactive | 2.11 Bn | -2.51 Bn | 344.40 Mn | 263.20 Mn |
| 8 | Unusual Machines | 1.19 Bn | 731.45 Mn | 5.80 Mn | 13.64 Mn |
| 9 | Sturm Ruger | 671.09 Mn | 275.09 Mn | 33.74 Mn | 26.11 Mn |
| 10 | Smith & Wesson Brands | 629.10 Mn | 541.36 Mn | 32.27 Mn | 28.05 Mn |
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 28.05 Mn |
| Apr 30, 2026 | 31.75 Mn |
| Jan 31, 2026 | 28.88 Mn |
| Oct 31, 2025 | 26.23 Mn |
| Jul 31, 2025 | 25.03 Mn |
| Apr 30, 2025 | 27.42 Mn |
| Jan 31, 2025 | 23.20 Mn |
| Oct 31, 2024 | 26.97 Mn |
| Jul 31, 2024 | 25.71 Mn |
| Apr 30, 2024 | 28.80 Mn |
| Jan 31, 2024 | 28.14 Mn |
| Oct 31, 2023 | 28.00 Mn |
| Jul 31, 2023 | 26.05 Mn |
| Apr 30, 2023 | 24.13 Mn |
| Jan 31, 2023 | 27.71 Mn |
| Oct 31, 2022 | 26.74 Mn |
| Jul 31, 2022 | 27.55 Mn |
| Apr 30, 2022 | 25.58 Mn |
| Jan 31, 2022 | 30.68 Mn |
| Oct 31, 2021 | 36.60 Mn |
Smith & Wesson 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=SWBI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SWBI", "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=SWBI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();