Moodys (MCO) Operating Expenses (2009 - 2026)
Moodys' Operating Expenses came in at $1.14 billion for Q2 2026, up 5.5% from $1.08 billion a year earlier but down 1.6% from the prior quarter.
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Analysis
Moodys (MCO) Operating Expenses (2009 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Moodys reported Operating Expenses of $4.51 billion, up 3.7% year-over-year; for FY2025, it came in at $4.37 billion, up 3.7% from FY2024.
- Operating Expenses has increased in each of the last eight years, with a five-year compound annual growth rate of 7.9% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $4.21 billion in FY2024 (+11.5%), $3.78 billion in FY2023 (+5.4%), $3.59 billion in FY2022 (+6.3%) and $3.37 billion in FY2021 (+13.1%).
- The five-year range for quarterly Operating Expenses is $850 million (Q3 2021) to $1.16 billion (Q1 2026).
- Year-over-year, Operating Expenses has increased for ten consecutive quarters, with growth averaging 7.0% over the last eight quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q4 2021 (growth of 21.2%), and the weakest in Q4 2022 (a decline of 4.0%).
- Business Quant data shows MCO's Operating Expenses at $1.16 billion (Q1 2026), $1.12 billion (Q4 2025) and $1.09 billion (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 2.35 Bn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 1.14 Bn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 379.50 Mn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 442.60 Mn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 1.39 Bn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 1.05 Bn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 456.62 Mn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 509.40 Mn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 3.69 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 599.10 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.14 Bn |
| Mar 31, 2026 | 1.16 Bn |
| Dec 31, 2025 | 1.12 Bn |
| Sep 30, 2025 | 1.09 Bn |
| Jun 30, 2025 | 1.08 Bn |
| Mar 31, 2025 | 1.08 Bn |
| Dec 31, 2024 | 1.11 Bn |
| Sep 30, 2024 | 1.08 Bn |
| Jun 30, 2024 | 1.04 Bn |
| Mar 31, 2024 | 985.00 Mn |
| Dec 31, 2023 | 982.00 Mn |
| Sep 30, 2023 | 937.00 Mn |
| Jun 30, 2023 | 944.00 Mn |
| Mar 31, 2023 | 916.00 Mn |
| Dec 31, 2022 | 984.00 Mn |
| Sep 30, 2022 | 862.00 Mn |
| Jun 30, 2022 | 873.00 Mn |
| Mar 31, 2022 | 866.00 Mn |
| Dec 31, 2021 | 1.03 Bn |
| Sep 30, 2021 | 850.00 Mn |
API Access
Moodys 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=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MCO", "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=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();