Cohen (COHN) Operating Expenses (2010 - 2026)
Cohen's Operating Expenses was $57.08 million in Q2 2026, up 9.0% from $52.38 million a year earlier and up 8.2% from the prior quarter.
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Analysis
Cohen (COHN) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Cohen's Operating Expenses was $245 million through Jun 30, 2026, up 89.1% year-over-year; for FY2025, it was $216.16 million, up 146.7% from FY2024.
- Operating Expenses has now increased for three consecutive years, with a five-year compound annual growth rate of 19.7% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $87.62 million in FY2024 (+15.1%), $76.12 million in FY2023 (+5.2%), $72.35 million in FY2022 (-32.2%) and $106.78 million in FY2021 (+21.6%).
- Quarterly Operating Expenses has moved between $15.22 million (Q4 2022) and $72.7 million (Q4 2025) over five years.
- Compared with a year earlier, Operating Expenses has increased for 12 straight quarters, with growth averaging 88.3% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q2 2025 (growth of 205.1%); the worst was Q4 2022 (a decline of 48.8%).
- Per Business Quant data, COHN's Operating Expenses in the three quarters before Q2 2026 was $52.77 million (Q1 2026), $72.7 million (Q4 2025) and $62.45 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Robinhood Markets | 104.48 Bn | 85.58 Bn | - | 734.00 Mn |
| 2 | Bank of New York Mellon | 99.71 Bn | 39.09 Bn | - | 3.44 Bn |
| 3 | Cme | 94.57 Bn | 94.57 Bn | - | 599.10 Mn |
| 4 | Intercontinental Exchange | 85.38 Bn | 79.19 Bn | - | 1.28 Bn |
| 5 | Nasdaq | 51.62 Bn | 49.05 Bn | 1.50 Bn | 788.00 Mn |
| 6 | State Street | 48.84 Bn | 48.84 Bn | - | 2.66 Bn |
| 7 | Interactive Brokers | 38.83 Bn | 32.29 Bn | - | 440.00 Mn |
| 8 | Northern Trust | 31.65 Bn | 31.65 Bn | - | 1.64 Bn |
| 9 | Cboe Global Markets | 27.38 Bn | 19.03 Bn | 731.60 Mn | 255.60 Mn |
| 10 | Cohen | 21.50 Mn | -723.18 Mn | - | 57.08 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 57.08 Mn |
| Mar 31, 2026 | 52.77 Mn |
| Dec 31, 2025 | 72.70 Mn |
| Sep 30, 2025 | 62.45 Mn |
| Jun 30, 2025 | 52.38 Mn |
| Mar 31, 2025 | 28.63 Mn |
| Dec 31, 2024 | 24.04 Mn |
| Sep 30, 2024 | 24.47 Mn |
| Jun 30, 2024 | 17.17 Mn |
| Mar 31, 2024 | 21.94 Mn |
| Dec 31, 2023 | 23.02 Mn |
| Sep 30, 2023 | 21.23 Mn |
| Jun 30, 2023 | 15.57 Mn |
| Mar 31, 2023 | 16.31 Mn |
| Dec 31, 2022 | 15.22 Mn |
| Sep 30, 2022 | 20.62 Mn |
| Jun 30, 2022 | 17.32 Mn |
| Mar 31, 2022 | 19.20 Mn |
| Dec 31, 2021 | 29.70 Mn |
| Sep 30, 2021 | 25.70 Mn |
API Access
Cohen 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=COHN&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "COHN", "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=COHN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();