Factset Research Systems (FDS) Total Liabilities (2010 - 2026)
Factset Research Systems' Total Liabilities was $2.16 billion in fiscal Q3 2026 (quarter ended May 31, 2026), down 1.1% from $2.18 billion a year earlier but up 3.3% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025 (ended Aug 31, 2025), Total Liabilities at Factset Research Systems came in at $2.12 billion, down 1.2% from FY2024.
- Total Liabilities has now declined for three consecutive fiscal years, though with a five-year compound annual growth rate of 12.3% (FY2020 to FY2025).
- In earlier fiscal years, Total Liabilities was $2.14 billion in FY2024 (-8.6%), $2.34 billion in FY2023 (-12.7%), $2.68 billion in FY2022 (+122.0%) and $1.21 billion in FY2021 (+1.8%).
- Quarterly Total Liabilities has moved between $1.16 billion (fiscal Q1 2022) and $2.8 billion (fiscal Q3 2022) over five years.
- Compared with a year earlier, Total Liabilities has declined for four straight quarters, with an average decline of 2.8% over the last eight quarters.
- The best year-over-year quarter for Total Liabilities over five years was fiscal Q3 2022 (growth of 135.9%); the worst was fiscal Q3 2023 (a decline of 16.8%).
- Per Business Quant data, FDS's Total Liabilities in the three fiscal quarters before Q3 2026 was $2.09 billion (Q2 2026), $2.05 billion (Q1 2026) and $2.12 billion (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 31.29 Bn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 11.51 Bn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 8.29 Bn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 5.68 Bn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 7.58 Bn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 7.19 Bn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 2.16 Bn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 2.92 Bn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 52.08 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 168.16 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 2.16 Bn |
| Feb 28, 2026 | 2.09 Bn |
| Nov 30, 2025 | 2.05 Bn |
| Aug 31, 2025 | 2.12 Bn |
| May 31, 2025 | 2.18 Bn |
| Feb 28, 2025 | 2.18 Bn |
| Nov 30, 2024 | 2.06 Bn |
| Aug 31, 2024 | 2.14 Bn |
| May 31, 2024 | 2.15 Bn |
| Feb 29, 2024 | 2.19 Bn |
| Nov 30, 2023 | 2.25 Bn |
| Aug 31, 2023 | 2.34 Bn |
| May 31, 2023 | 2.33 Bn |
| Feb 28, 2023 | 2.39 Bn |
| Nov 30, 2022 | 2.50 Bn |
| Aug 31, 2022 | 2.68 Bn |
| May 31, 2022 | 2.80 Bn |
| Feb 28, 2022 | 1.16 Bn |
| Nov 30, 2021 | 1.16 Bn |
| Aug 31, 2021 | 1.21 Bn |
API Access
Factset Research Systems Total Liabilities 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=total-liabilities&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "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=total-liabilities&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();