Factset Research Systems (FDS) Return on Sales [ROS] (2009 - 2026)
Factset Research Systems (FDS) posted Return on Sales [ROS] of 24.68% for fiscal Q4 2026 (quarter ended Aug 31, 2026), down 5.03 percentage points from 29.71% a year earlier and down 2.02 percentage points from the prior quarter.
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Analysis
Factset Research Systems (FDS) Return on Sales [ROS] (2009 - 2026) Analysis & Trends
For FY2026 (ended Aug 31, 2026), Factset Research Systems' Return on Sales [ROS] came in at 28.27%, down 3.96 percentage points from FY2025.
- Annual Return on Sales [ROS] shows a five-year change of -1.52 percentage points (FY2021 to FY2026).
- In prior fiscal years, Factset Research Systems' Return on Sales [ROS] was 32.23% in FY2025 (+0.40 pp), 31.83% in FY2024 (+1.66 pp), 30.17% in FY2023 (+4.38 pp) and 25.79% in FY2022 (-4.00 pp).
- The fiscal Q4 2026 figure stands as the lowest quarterly Return on Sales [ROS] since fiscal Q4 2024.
- On a year-over-year basis, Return on Sales [ROS] has declined in each of the last four quarters, with an average year-over-year change of -1.79 percentage points over the last eight quarters.
- The strongest year-over-year quarter for Return on Sales [ROS] in the past five years was fiscal Q3 2023, with a gain of 12.56 percentage points; the weakest was fiscal Q3 2022, with a drop of 9.56 percentage points.
- According to Business Quant data, Return on Sales [ROS] for the three prior fiscal quarters was 26.70% (Q3 2026), 30.27% (Q2 2026) and 31.61% (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 132.72 Bn | 128.33 Bn | 2.98 Bn | 43.70% |
| 2 | Moodys | 83.35 Bn | 76.42 Bn | 1.50 Bn | 44.78% |
| 3 | Msci | 44.14 Bn | 42.62 Bn | 739.80 Mn | 56.29% |
| 4 | Verisk Analytics | 25.49 Bn | 20.13 Bn | 572.90 Mn | 45.11% |
| 5 | Equifax | 21.35 Bn | 20.74 Bn | 940.10 Mn | 20.99% |
| 6 | TransUnion | 15.61 Bn | 12.59 Bn | - | 19.70% |
| 7 | Factset Research Systems | 11.68 Bn | 10.53 Bn | 308.40 Mn | 24.68% |
| 8 | Morningstar | 8.44 Bn | 6.34 Bn | 423.90 Mn | 24.22% |
| 9 | Mastercard | 542.86 Bn | 502.77 Bn | - | 60.22% |
| 10 | Cme | 109.27 Bn | 97.61 Bn | - | 65.70% |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 24.68% |
| May 31, 2026 | 26.70% |
| Feb 28, 2026 | 30.27% |
| Nov 30, 2025 | 31.61% |
| Aug 31, 2025 | 29.71% |
| May 31, 2025 | 33.16% |
| Feb 28, 2025 | 32.50% |
| Nov 30, 2024 | 33.65% |
| Aug 31, 2024 | 22.74% |
| May 31, 2024 | 36.63% |
| Feb 29, 2024 | 33.33% |
| Nov 30, 2023 | 34.86% |
| Aug 31, 2023 | 21.67% |
| May 31, 2023 | 32.46% |
| Feb 28, 2023 | 32.86% |
| Nov 30, 2022 | 34.05% |
| Aug 31, 2022 | 26.48% |
| May 31, 2022 | 19.90% |
| Feb 28, 2022 | 28.61% |
| Nov 30, 2021 | 28.88% |
API Access
Factset Research Systems 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=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "ticker": "FDS", "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=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();