Factset Research Systems (FDS) Receivables (2010 - 2026)
Factset Research Systems' Receivables came in at $289.99 million for fiscal Q3 2026 (quarter ended May 31, 2026), up 6.7% from $271.85 million a year earlier but down 9.4% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Receivables (2010 - 2026) Analysis & Trends
At the end of FY2025 (ended Aug 31, 2025), Factset Research Systems' Receivables was $270.68 million, up 18.7% from FY2024.
- Receivables carries a five-year compound annual growth rate of 9.5% (FY2020 to FY2025).
- Going back by fiscal year, Receivables was $228.05 million in FY2024 (-4.0%), $237.67 million in FY2023 (+16.4%), $204.1 million in FY2022 (+20.4%) and $169.49 million in FY2021 (-1.5%).
- The five-year range for quarterly Receivables is $157.39 million (fiscal Q1 2022) to $320.23 million (fiscal Q2 2026).
- Year-over-year, Receivables has increased for seven consecutive quarters, with growth averaging 8.7% over the last eight quarters.
- The fastest year-over-year change in Receivables over five years came in fiscal Q1 2023 (growth of 44.5%), and the weakest in fiscal Q4 2024 (a decline of 4.0%).
- Business Quant data shows FDS's Receivables at $320.23 million (Q2 2026), $289 million (Q1 2026) and $270.68 million (Q4 2025) in the three fiscal quarters before Q3 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Receivables (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 115.73 Bn | 111.34 Bn | 2.98 Bn | 3.44 Bn |
| 2 | Moodys | 78.77 Bn | 71.20 Bn | 1.67 Bn | 2.58 Bn |
| 3 | Msci | 39.23 Bn | 37.58 Bn | 717.10 Mn | 884.40 Mn |
| 4 | Verisk Analytics | 21.59 Bn | 16.23 Bn | 572.90 Mn | 400.00 Mn |
| 5 | Equifax | 16.56 Bn | 15.96 Bn | 926.40 Mn | 1.16 Bn |
| 6 | TransUnion | 12.32 Bn | 9.30 Bn | - | 1.05 Bn |
| 7 | Factset Research Systems | 9.30 Bn | 8.06 Bn | 310.73 Mn | 289.99 Mn |
| 8 | Morningstar | 6.99 Bn | 4.89 Bn | 423.90 Mn | 382.90 Mn |
| 9 | Mastercard | 490.78 Bn | 450.70 Bn | - | 5.05 Bn |
| 10 | Cme | 94.57 Bn | 94.57 Bn | - | 753.10 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 289.99 Mn |
| Feb 28, 2026 | 320.23 Mn |
| Nov 30, 2025 | 289.00 Mn |
| Aug 31, 2025 | 270.68 Mn |
| May 31, 2025 | 271.85 Mn |
| Feb 28, 2025 | 277.64 Mn |
| Nov 30, 2024 | 252.52 Mn |
| Aug 31, 2024 | 228.05 Mn |
| May 31, 2024 | 240.10 Mn |
| Feb 29, 2024 | 272.18 Mn |
| Nov 30, 2023 | 245.32 Mn |
| Aug 31, 2023 | 237.67 Mn |
| May 31, 2023 | 237.79 Mn |
| Feb 28, 2023 | 257.41 Mn |
| Nov 30, 2022 | 227.49 Mn |
| Aug 31, 2022 | 204.10 Mn |
| May 31, 2022 | 226.49 Mn |
| Feb 28, 2022 | 188.31 Mn |
| Nov 30, 2021 | 157.39 Mn |
| Aug 31, 2021 | 169.49 Mn |
API Access
Factset Research Systems Receivables 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=receivables&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "receivables", "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=receivables&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();