American Eagle Outfitters (AEO) Operating Expenses (2009 - 2026)
American Eagle Outfitters (AEO) recorded Operating Expenses of $408.35 million in fiscal Q2 2027 (quarter ended Aug 1, 2026), up 19.3% from $342.21 million a year earlier and up 8.5% from the prior quarter.
American Eagle Outfitters (AEO) Operating Expenses (2009 - 2026) Analysis & Trends
On a TTM basis, American Eagle Outfitters' Operating Expenses came in at $1.59 billion as of Aug 1, 2026, up 10.8% year-over-year; for FY2026 (ended Jan 31, 2026), it was $1.7 billion, up 3.2% from FY2025.
- Annual Operating Expenses has a five-year compound annual growth rate of 8.3% (FY2021 to FY2026).
- Across earlier fiscal years, Operating Expenses came in at $1.65 billion in FY2025 (-8.7%), $1.81 billion in FY2024 (+20.4%), $1.5 billion in FY2023 (+7.4%) and $1.4 billion in FY2022 (+22.4%).
- Quarterly Operating Expenses has ranged from $298.76 million in fiscal Q1 2023 to $427.09 million in fiscal Q4 2024 over the past five years.
- On a year-over-year basis, Operating Expenses has increased for four consecutive quarters, with growth averaging 4.5% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 21.5% in fiscal Q4 2024, against a decline of 6.0% in fiscal Q4 2025 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $376.49 million (Q1 2027), $418.2 million (Q4 2026) and $386.34 million (Q3 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,686.58 Bn | 2,203.28 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 283.87 Bn | 277.11 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 145.82 Bn | 123.36 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 103.43 Bn | 96.39 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 74.61 Bn | 57.54 Bn | 2.12 Bn | 1.02 Bn |
| 6 | Target | 71.16 Bn | 65.74 Bn | 8.94 Bn | 5.73 Bn |
| 7 | O Reilly Automotive | 69.71 Bn | 68.80 Bn | 2.52 Bn | 1.53 Bn |
| 8 | Carvana | 68.78 Bn | 60.38 Bn | 1.38 Bn | 704.00 Mn |
| 9 | Autozone | 46.23 Bn | 45.13 Bn | 2.52 Bn | 1.60 Bn |
| 10 | American Eagle Outfitters | 2.98 Bn | 2.37 Bn | 672.06 Mn | 408.35 Mn |
Historic Data
| Date | Value |
|---|---|
| Aug 1, 2026 | 408.35 Mn |
| May 2, 2026 | 376.49 Mn |
| Jan 31, 2026 | 418.20 Mn |
| Nov 1, 2025 | 386.34 Mn |
| Aug 2, 2025 | 342.21 Mn |
| May 3, 2025 | 338.79 Mn |
| Feb 1, 2025 | 401.63 Mn |
| Nov 2, 2024 | 351.38 Mn |
| Aug 3, 2024 | 345.31 Mn |
| May 4, 2024 | 333.49 Mn |
| Feb 3, 2024 | 427.09 Mn |
| Oct 28, 2023 | 361.99 Mn |
| Jul 29, 2023 | 331.87 Mn |
| Apr 29, 2023 | 333.62 Mn |
| Jan 28, 2023 | 351.41 Mn |
| Oct 29, 2022 | 311.10 Mn |
| Jul 30, 2022 | 307.83 Mn |
| Apr 30, 2022 | 298.76 Mn |
| Jan 29, 2022 | 349.68 Mn |
| Oct 30, 2021 | 313.89 Mn |
American Eagle Outfitters 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=AEO&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "AEO", "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=AEO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();