Cboe Global Markets (CBOE) Shares Outstanding (2009 - 2026)
Cboe Global Markets (CBOE) posted Weighted Average Shares Outstanding of 104.5 million for Q3 2026, down 0.1% from 104.6 million a year earlier and down 0.2% from the prior quarter.
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Analysis
Cboe Global Markets (CBOE) Shares Outstanding (2009 - 2026) Analysis & Trends
For FY2025, Cboe Global Markets' Weighted Average Shares Outstanding came in at 104.7 million, down 0.4% from FY2024.
- Annual Weighted Average Shares Outstanding has declined for seven consecutive years, with a five-year compound annual growth rate of -0.8% (FY2020 to FY2025).
- In prior years, Cboe Global Markets' Weighted Average Shares Outstanding was 105.1 million in FY2024 (-0.7%), 105.8 million in FY2023 (-0.5%), 106.3 million in FY2022 (-0.7%) and 107 million in FY2021 (-1.9%).
- The Q3 2026 figure stands as the lowest quarterly Weighted Average Shares Outstanding since Q1 2017.
- Over the past five years, the year-over-year decline in Weighted Average Shares Outstanding ranged from 0.0% (Q2 2026) to 1.9% (Q4 2021).
- According to Business Quant data, Weighted Average Shares Outstanding for the three prior quarters was 104.7 million (Q2 2026), 104.7 million (Q1 2026) and 104.7 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Shares Outstanding (Qtr) |
|---|---|---|---|---|---|
| 1 | Cme | 110.48 Bn | 98.81 Bn | 359.20 Mn | 358.02 Mn |
| 2 | Bank of New York Mellon | 101.69 Bn | 35.78 Bn | - | 679.49 Mn |
| 3 | Intercontinental Exchange | 98.41 Bn | 92.22 Bn | 1.22 Bn | 561.00 Mn |
| 4 | Robinhood Markets | 73.28 Bn | 52.89 Bn | - | 898.00 Mn |
| 5 | Nasdaq | 61.44 Bn | 58.88 Bn | 1.48 Bn | 558.93 Mn |
| 6 | State Street | 46.84 Bn | 43.46 Bn | - | 274.18 Mn |
| 7 | Cboe Global Markets | 38.04 Bn | 28.52 Bn | 712.20 Mn | 104.50 Mn |
| 8 | Interactive Brokers | 35.16 Bn | 18.81 Bn | - | 452.27 Mn |
| 9 | Northern Trust | 32.22 Bn | 32.22 Bn | - | 182.04 Mn |
| 10 | Tradeweb Markets | 30.09 Bn | 22.61 Bn | - | 210.41 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 104.50 Mn |
| Jun 30, 2026 | 104.70 Mn |
| Mar 31, 2026 | 104.70 Mn |
| Dec 31, 2025 | 104.70 Mn |
| Sep 30, 2025 | 104.60 Mn |
| Jun 30, 2025 | 104.70 Mn |
| Mar 31, 2025 | 104.70 Mn |
| Dec 31, 2024 | 105.10 Mn |
| Sep 30, 2024 | 104.70 Mn |
| Jun 30, 2024 | 105.10 Mn |
| Mar 31, 2024 | 105.60 Mn |
| Dec 31, 2023 | 105.80 Mn |
| Sep 30, 2023 | 105.70 Mn |
| Jun 30, 2023 | 105.70 Mn |
| Mar 31, 2023 | 105.90 Mn |
| Dec 31, 2022 | 106.30 Mn |
| Sep 30, 2022 | 106.20 Mn |
| Jun 30, 2022 | 106.30 Mn |
| Mar 31, 2022 | 106.60 Mn |
| Dec 31, 2021 | 107.00 Mn |
API Access
Cboe Global Markets Shares Outstanding 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=shares-outstanding&ticker=CBOE&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "shares-outstanding", "ticker": "CBOE", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=shares-outstanding&ticker=CBOE&period=max&api_key=YOUR_API_KEY");
const data = await res.json();