A.K.A. Brands Holding (AKA) Operating Expenses (2020 - 2026)
A.K.A. Brands Holding's Operating Expenses came in at $96.68 million for Q2 2026, up 4.1% from $92.84 million a year earlier and up 10.2% from the prior quarter.
A.K.A. Brands Holding (AKA) Operating Expenses (2020 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, A.K.A. Brands Holding reported Operating Expenses of $374.65 million, up 6.3% year-over-year; for FY2025, it came in at $362.11 million, up 7.2% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 28.3% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $337.83 million in FY2024 (-12.0%), $383.69 million in FY2023 (-24.7%), $509.29 million in FY2022 (+74.8%) and $291.28 million in FY2021 (+179.4%).
- The five-year range for quarterly Operating Expenses is $71.77 million (Q1 2024) to $254.3 million (Q4 2022).
- Year-over-year, Operating Expenses has increased for three consecutive quarters, with growth averaging 2.1% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q3 2021 (growth of 207.6%), and the weakest in Q4 2023 (a decline of 67.8%).
- Business Quant data shows AKA's Operating Expenses at $87.73 million (Q1 2026), $101.82 million (Q4 2025) and $88.41 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nike | 55.76 Bn | 21.96 Bn | 4.80 Bn | 3.91 Bn |
| 2 | Tapestry | 23.84 Bn | 19.80 Bn | 1.56 Bn | 1.12 Bn |
| 3 | Ralph Lauren | 22.96 Bn | 15.05 Bn | 1.44 Bn | 1.10 Bn |
| 4 | Deckers Outdoor | 12.32 Bn | 5.19 Bn | - | - |
| 5 | Lululemon Athletica | 10.96 Bn | 5.21 Bn | 1.46 Bn | 1.01 Bn |
| 6 | Levi Strauss | 8.19 Bn | 4.77 Bn | 1.07 Bn | 843.50 Mn |
| 7 | V F | 6.78 Bn | 3.40 Bn | 917.04 Mn | 1.75 Bn |
| 8 | Gildan Activewear | 6.51 Bn | 5.43 Bn | 459.76 Mn | 283.86 Mn |
| 9 | Birkenstock Holding | 6.05 Bn | 4.37 Bn | 493.89 Mn | 37.80 Mn |
| 10 | A.K.A. Brands Holding | 114.95 Mn | 37.34 Mn | 97.84 Mn | 96.68 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 96.68 Mn |
| Mar 31, 2026 | 87.73 Mn |
| Dec 31, 2025 | 101.82 Mn |
| Sep 30, 2025 | 88.41 Mn |
| Jun 30, 2025 | 92.84 Mn |
| Mar 31, 2025 | 79.04 Mn |
| Dec 31, 2024 | 91.73 Mn |
| Sep 30, 2024 | 88.99 Mn |
| Jun 30, 2024 | 85.33 Mn |
| Mar 31, 2024 | 71.77 Mn |
| Dec 31, 2023 | 81.84 Mn |
| Sep 30, 2023 | 148.32 Mn |
| Jun 30, 2023 | 78.48 Mn |
| Mar 31, 2023 | 75.05 Mn |
| Dec 31, 2022 | 254.30 Mn |
| Sep 30, 2022 | 84.12 Mn |
| Jun 30, 2022 | 90.02 Mn |
| Mar 31, 2022 | 80.85 Mn |
| Dec 31, 2021 | 94.28 Mn |
| Sep 30, 2021 | 84.95 Mn |
A.K.A. Brands Holding 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=AKA&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "AKA", "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=AKA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();