Factset Research Systems (FDS) Tax Provisions (2009 - 2026)
Factset Research Systems (FDS) recorded Tax Provisions of $27.4 million in fiscal Q3 2026 (quarter ended May 31, 2026), down 12.7% from $31.41 million a year earlier and down 2.3% from the prior quarter.
Factset Research Systems (FDS) Tax Provisions (2009 - 2026) Analysis & Trends
On a TTM basis, Factset Research Systems' Tax Provisions came in at $128.33 million as of May 31, 2026, up 10.4% year-over-year; for FY2025 (ended Aug 31, 2025), it came in at $123.92 million, up 8.3% from FY2024.
- Annual Tax Provisions has a five-year compound annual growth rate of 18.0% (FY2020 to FY2025).
- Across earlier fiscal years, Tax Provisions came in at $114.38 million in FY2024 (-1.2%), $115.78 million in FY2023 (+148.0%), $46.68 million in FY2022 (-31.4%) and $68.03 million in FY2021 (+25.5%).
- The fiscal Q3 2026 figure is the lowest quarterly Tax Provisions since fiscal Q1 2024.
- On a year-over-year basis, Tax Provisions rose in four of the last eight quarters, with growth averaging 2.1%.
- Peak year-over-year performance for Tax Provisions in the last five years was growth of 251.4% in fiscal Q4 2023, against a decline of 35.4% in fiscal Q1 2022 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $28.06 million (Q2 2026), $37.53 million (Q1 2026) and $35.34 million (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Taxes (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 406.00 Mn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 292.00 Mn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 75.30 Mn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 74.80 Mn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 71.80 Mn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 54.80 Mn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 27.40 Mn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 38.30 Mn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 1.10 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 285.90 Mn |
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 27.40 Mn |
| Feb 28, 2026 | 28.06 Mn |
| Nov 30, 2025 | 37.53 Mn |
| Aug 31, 2025 | 35.34 Mn |
| May 31, 2025 | 31.41 Mn |
| Feb 28, 2025 | 27.46 Mn |
| Nov 30, 2024 | 29.72 Mn |
| Aug 31, 2024 | 27.63 Mn |
| May 31, 2024 | 32.40 Mn |
| Feb 29, 2024 | 27.71 Mn |
| Nov 30, 2023 | 26.64 Mn |
| Aug 31, 2023 | 42.19 Mn |
| May 31, 2023 | 27.34 Mn |
| Feb 28, 2023 | 25.17 Mn |
| Nov 30, 2022 | 21.09 Mn |
| Aug 31, 2022 | 12.01 Mn |
| May 31, 2022 | 10.37 Mn |
| Feb 28, 2022 | 12.02 Mn |
| Nov 30, 2021 | 12.30 Mn |
| Aug 31, 2021 | 17.38 Mn |
Factset Research Systems Tax Provisions 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=tax-provisions&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "tax-provisions", "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=tax-provisions&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();