Factset Research Systems (FDS) Receivables - Net (2010 - 2026)
Factset Research Systems (FDS) recorded Receivables - Net of $298.62 million in fiscal Q4 2026 (quarter ended Aug 31, 2026), up 10.3% from $270.68 million a year earlier and up 3.0% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Receivables - Net (2010 - 2026) Analysis & Trends
Starting with fiscal Q4 2010, Factset Research Systems' Receivables - Net history includes 65 quarters.
- Annual Receivables - Net has a five-year compound annual growth rate of 14.6% (FY2021 to FY2026).
- Across earlier fiscal years, Receivables - Net came in at $270.68 million in FY2025 (+18.7%), $228.05 million in FY2024 (-4.0%), $237.67 million in FY2023 (+16.4%) and $204.1 million in FY2022 (+35.0%).
- Quarterly Receivables - Net has ranged from $157.39 million in fiscal Q1 2022 to $320.23 million in fiscal Q2 2026 over the past five years.
- On a year-over-year basis, Receivables - Net has increased for eight consecutive quarters, with growth averaging 10.5% over the last eight quarters.
- Peak year-over-year performance for Receivables - Net in the last five years was growth of 44.5% in fiscal Q1 2023, against a decline of 4.0% in fiscal Q4 2024 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $289.99 million (Q3 2026), $320.23 million (Q2 2026) and $289 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | S&P Global | 136.14 Bn | 125.99 Bn | 2.64 Bn |
| 2 | Moodys | 87.37 Bn | 80.29 Bn | 1.50 Bn |
| 3 | Msci | 46.53 Bn | 45.01 Bn | 739.80 Mn |
| 4 | Verisk Analytics | 25.76 Bn | 20.40 Bn | 572.90 Mn |
| 5 | Equifax | 21.57 Bn | 20.84 Bn | 940.10 Mn |
| 6 | TransUnion | 15.42 Bn | 12.11 Bn | - |
| 7 | Factset Research Systems | 11.51 Bn | 10.36 Bn | 308.40 Mn |
| 8 | Morningstar | 9.02 Bn | 6.91 Bn | 431.40 Mn |
| 9 | Mastercard | 571.76 Bn | 531.85 Bn | - |
| 10 | Cme | 109.34 Bn | 97.67 Bn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 298.62 Mn |
| 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 |
API Access
Factset Research Systems Receivables - Net 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-net&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "receivables-net", "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-net&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();