Moodys (MCO) Return on Sales [ROS] (2009 - 2026)
Moodys (MCO) reported Return on Sales [ROS] of 47.87% for Q2 2026, up 4.77 percentage points from 43.10% a year earlier and up 3.52 percentage points from the prior quarter.
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Analysis
Moodys (MCO) Return on Sales [ROS] (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Moodys' Return on Sales [ROS] came in at 44.79%, up 4.24 percentage points year-over-year; for FY2025, it came in at 43.42%, up 2.86 percentage points from FY2024.
- Return on Sales [ROS] has increased for three consecutive years, though with a five-year change of -1.04 percentage points (FY2020 to FY2025).
- By year, Return on Sales [ROS] came in at 40.56% in FY2024 (+4.44 pp), 36.12% in FY2023 (+1.69 pp), 34.44% in FY2022 (-11.30 pp) and 45.74% in FY2021 (+1.28 pp).
- The Q2 2026 figure ranks as the highest quarterly Return on Sales [ROS] since Q2 2021.
- Year over year, Return on Sales [ROS] has now increased in each of the last five quarters, with an average year-over-year change of +2.65 percentage points over the last eight quarters.
- The high point for year-over-year Return on Sales [ROS] in five years was Q4 2023 (a gain of 9.93 percentage points); the low point was Q2 2022 (a drop of 14.79 percentage points).
- Per Business Quant data, the three quarters before Q2 2026 came in at 44.35% (Q1 2026), 40.76% (Q4 2025) and 45.69% (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 113.90 Bn | 109.50 Bn | 2.98 Bn | 43.70% |
| 2 | Moodys | 76.48 Bn | 68.91 Bn | 1.67 Bn | 47.87% |
| 3 | Msci | 38.99 Bn | 37.33 Bn | 717.10 Mn | 56.23% |
| 4 | Verisk Analytics | 21.33 Bn | 15.96 Bn | 572.90 Mn | 45.11% |
| 5 | Equifax | 16.47 Bn | 15.87 Bn | 926.40 Mn | 18.48% |
| 6 | TransUnion | 12.16 Bn | 9.14 Bn | - | 19.70% |
| 7 | Factset Research Systems | 9.52 Bn | 8.28 Bn | 310.73 Mn | 26.70% |
| 8 | Morningstar | 6.89 Bn | 4.79 Bn | 423.90 Mn | 24.22% |
| 9 | Mastercard | 480.84 Bn | 440.75 Bn | - | 60.22% |
| 10 | Cme | 94.54 Bn | 94.54 Bn | - | 64.89% |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 47.87% |
| Mar 31, 2026 | 44.35% |
| Dec 31, 2025 | 40.76% |
| Sep 30, 2025 | 45.69% |
| Jun 30, 2025 | 43.10% |
| Mar 31, 2025 | 43.97% |
| Dec 31, 2024 | 33.55% |
| Sep 30, 2024 | 40.71% |
| Jun 30, 2024 | 42.65% |
| Mar 31, 2024 | 44.85% |
| Dec 31, 2023 | 33.65% |
| Sep 30, 2023 | 36.35% |
| Jun 30, 2023 | 36.81% |
| Mar 31, 2023 | 37.69% |
| Dec 31, 2022 | 23.72% |
| Sep 30, 2022 | 32.39% |
| Jun 30, 2022 | 36.78% |
| Mar 31, 2022 | 43.10% |
| Dec 31, 2021 | 33.40% |
| Sep 30, 2021 | 44.30% |
API Access
Moodys Return on Sales [ROS] 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=return-on-sales-%5Bros%5D&ticker=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "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=return-on-sales-%5Bros%5D&ticker=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();