Factset Research Systems (FDS) Dividends payables (2009 - 2026)
Factset Research Systems' Dividends payables was $40.9 million in fiscal Q4 2026 (quarter ended Aug 31, 2026), down 1.2% from $41.41 million a year earlier and down 1.4% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Dividends payables (2009 - 2026) Analysis & Trends
From fiscal Q4 2010 onward, Factset Research Systems has reported Dividends payables for 65 quarters.
- Dividends payables shows a five-year compound annual growth rate of 5.8% (FY2021 to FY2026).
- In earlier fiscal years, Dividends payables was $41.41 million in FY2025 (+4.9%), $39.47 million in FY2024 (+5.9%), $37.27 million in FY2023 (+10.1%) and $33.86 million in FY2022 (+9.8%).
- Quarterly Dividends payables has moved between $30.97 million (fiscal Q1 2022) and $41.64 million (fiscal Q3 2025) over five years.
- Compared with a year earlier, Dividends payables was higher in six of the last eight quarters, with growth averaging 3.2%.
- The best year-over-year quarter for Dividends payables over five years was fiscal Q3 2023 (growth of 10.8%); the worst was fiscal Q4 2026 (a decline of 1.2%).
- Per Business Quant data, FDS's Dividends payables in the three fiscal quarters before Q4 2026 was $41.5 million (Q3 2026), $40.31 million (Q2 2026) and $40.97 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Dividends payables (Qtr) |
|---|---|---|---|---|---|
| 1 | Msci | 236.00 Bn | 234.48 Bn | 739.80 Mn | - |
| 2 | S&P Global | 137.30 Bn | 132.50 Bn | 2.64 Bn | - |
| 3 | Moodys | 87.63 Bn | 80.69 Bn | 1.50 Bn | - |
| 4 | Verisk Analytics | 26.66 Bn | 22.85 Bn | 567.00 Mn | - |
| 5 | Equifax | 22.03 Bn | 21.43 Bn | 940.10 Mn | - |
| 6 | TransUnion | 15.46 Bn | 12.30 Bn | - | - |
| 7 | Factset Research Systems | 11.73 Bn | 10.58 Bn | 308.40 Mn | 40.90 Mn |
| 8 | Morningstar | 9.14 Bn | 7.03 Bn | 431.40 Mn | - |
| 9 | Mastercard | 572.29 Bn | 532.37 Bn | - | - |
| 10 | Cme | 113.65 Bn | 101.99 Bn | 359.20 Mn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 40.90 Mn |
| May 31, 2026 | 41.50 Mn |
| Feb 28, 2026 | 40.31 Mn |
| Nov 30, 2025 | 40.97 Mn |
| Aug 31, 2025 | 41.41 Mn |
| May 31, 2025 | 41.64 Mn |
| Feb 28, 2025 | 39.51 Mn |
| Nov 30, 2024 | 39.57 Mn |
| Aug 31, 2024 | 39.47 Mn |
| May 31, 2024 | 39.59 Mn |
| Feb 29, 2024 | 37.36 Mn |
| Nov 30, 2023 | 37.30 Mn |
| Aug 31, 2023 | 37.27 Mn |
| May 31, 2023 | 37.44 Mn |
| Feb 28, 2023 | 34.10 Mn |
| Nov 30, 2022 | 34.01 Mn |
| Aug 31, 2022 | 33.86 Mn |
| May 31, 2022 | 33.80 Mn |
| Feb 28, 2022 | 31.07 Mn |
| Nov 30, 2021 | 30.97 Mn |
API Access
Factset Research Systems Dividends payables 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=dividends-payables&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "dividends-payables", "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=dividends-payables&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();