Ames National (ATLO) Operating Expenses (2009 - 2026)
Ames National's Operating Expenses was $11.23 million in Q3 2026, up 9.6% from $10.24 million a year earlier and up 0.5% from the prior quarter.
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Analysis
Ames National (ATLO) Operating Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Ames National's Operating Expenses was $44.33 million through Sep 30, 2026, up 7.1% year-over-year; for FY2025, it came in at $41.93 million, down 0.1% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 2.8% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $41.98 million in FY2024 (+4.5%), $40.16 million in FY2023 (+3.9%), $38.64 million in FY2022 (+5.5%) and $36.62 million in FY2021 (+0.2%).
- The Q3 2026 figure marks the highest quarterly Operating Expenses in data going back to Q2 2010.
- Compared with a year earlier, Operating Expenses has increased for four straight quarters, with growth averaging 3.5% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q3 2026 (growth of 9.6%); the worst was Q2 2025 (a decline of 3.5%).
- Per Business Quant data, ATLO's Operating Expenses in the three quarters before Q3 2026 was $11.17 million (Q2 2026), $10.87 million (Q1 2026) and $11.06 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 909.02 Bn | 886.77 Bn | - | 27.30 Bn |
| 2 | Banco Santander Chile | 406.67 Bn | -7,110.43 Bn | - | - |
| 3 | Bank Of America | 350.91 Bn | -2,025.20 Bn | - | 19.72 Bn |
| 4 | Hsbc Holdings | 308.71 Bn | -2,966.46 Bn | - | 8.82 Bn |
| 5 | Morgan Stanley | 293.66 Bn | -249.15 Bn | - | 13.71 Bn |
| 6 | Royal Bank Of Canada | 283.56 Bn | -546.44 Bn | - | 7.02 Bn |
| 7 | Goldman Sachs | 262.71 Bn | -3,480.74 Bn | - | 11.31 Bn |
| 8 | Wells Fargo & Company | 257.44 Bn | 92.87 Bn | - | 13.74 Bn |
| 9 | Mitsubishi Ufj Financial | 256.82 Bn | -75.42 Bn | 10.89 Bn | 17.50 Bn |
| 10 | Ames National | 301.77 Mn | -125.59 Mn | - | 11.23 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 11.23 Mn |
| Jun 30, 2026 | 11.17 Mn |
| Mar 31, 2026 | 10.87 Mn |
| Dec 31, 2025 | 11.06 Mn |
| Sep 30, 2025 | 10.24 Mn |
| Jun 30, 2025 | 10.36 Mn |
| Mar 31, 2025 | 10.26 Mn |
| Dec 31, 2024 | 10.54 Mn |
| Sep 30, 2024 | 10.51 Mn |
| Jun 30, 2024 | 10.74 Mn |
| Mar 31, 2024 | 10.19 Mn |
| Dec 31, 2023 | 10.02 Mn |
| Sep 30, 2023 | 9.80 Mn |
| Jun 30, 2023 | 10.56 Mn |
| Mar 31, 2023 | 9.78 Mn |
| Dec 31, 2022 | 9.93 Mn |
| Sep 30, 2022 | 9.49 Mn |
| Jun 30, 2022 | 9.85 Mn |
| Mar 31, 2022 | 9.38 Mn |
| Dec 31, 2021 | 9.30 Mn |
API Access
Ames National 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=ATLO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ATLO", "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=ATLO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();