Factset Research Systems (FDS) Change in Accured Expenses (2009 - 2026)
Factset Research Systems' Change in Accured Expenses was $65.03 million in fiscal Q3 2026 (quarter ended May 31, 2026), up 433.2% from $12.2 million a year earlier and up 131.7% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Change in Accured Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Factset Research Systems' Change in Accured Expenses was $13.11 million through May 31, 2026, down 66.3% year-over-year; for FY2025 (ended Aug 31, 2025), it was -$59.4 million.
- In earlier fiscal years, Change in Accured Expenses was $55.35 million in FY2024 (+559.4%), $8.39 million in FY2023 (-42.2%), $14.52 million in FY2022 (-33.4%) and $21.82 million in FY2021 (+32.6%).
- The fiscal Q3 2026 figure marks the highest quarterly Change in Accured Expenses since fiscal Q4 2024.
- The best year-over-year quarter for Change in Accured Expenses over five years was fiscal Q3 2026 (growth of 433.2%); the worst was fiscal Q3 2024 (a decline of 39.5%).
- Per Business Quant data, FDS's Change in Accured Expenses in the three fiscal quarters before Q3 2026 was $28.07 million (Q2 2026), -$70.31 million (Q1 2026) and -$9.68 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 113.90 Bn | 109.50 Bn | 2.98 Bn | 243.00 Mn |
| 2 | Moodys | 76.48 Bn | 68.91 Bn | 1.67 Bn | -18.00 Mn |
| 3 | Msci | 38.99 Bn | 37.33 Bn | 717.10 Mn | 61.50 Mn |
| 4 | Verisk Analytics | 21.33 Bn | 15.96 Bn | 572.90 Mn | 59.70 Mn |
| 5 | Equifax | 16.47 Bn | 15.87 Bn | 926.40 Mn | - |
| 6 | TransUnion | 12.16 Bn | 9.14 Bn | - | - |
| 7 | Factset Research Systems | 9.52 Bn | 8.28 Bn | 310.73 Mn | 65.03 Mn |
| 8 | Morningstar | 6.89 Bn | 4.79 Bn | 423.90 Mn | 40.60 Mn |
| 9 | Mastercard | 480.84 Bn | 440.75 Bn | - | 749.00 Mn |
| 10 | Cme | 94.54 Bn | 94.54 Bn | - | -70.30 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 65.03 Mn |
| Feb 28, 2026 | 28.07 Mn |
| Nov 30, 2025 | -70.31 Mn |
| Aug 31, 2025 | -9.68 Mn |
| May 31, 2025 | 12.20 Mn |
| Feb 28, 2025 | -21.25 Mn |
| Nov 30, 2024 | -40.66 Mn |
| Aug 31, 2024 | 88.68 Mn |
| May 31, 2024 | 7.13 Mn |
| Feb 29, 2024 | 19.89 Mn |
| Nov 30, 2023 | -60.35 Mn |
| Aug 31, 2023 | 48.33 Mn |
| May 31, 2023 | 11.78 Mn |
| Feb 28, 2023 | 15.08 Mn |
| Nov 30, 2022 | -66.80 Mn |
| Aug 31, 2022 | 38.52 Mn |
| May 31, 2022 | 10.69 Mn |
| Feb 28, 2022 | 18.78 Mn |
| Nov 30, 2021 | -53.46 Mn |
| Aug 31, 2021 | 32.88 Mn |
API Access
Factset Research Systems Change in Accured Expenses 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=change-in-accured-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "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=change-in-accured-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();