Factset Research Systems (FDS) EBITDA (2009 - 2026)
Factset Research Systems (FDS) posted EBITDA of $212.17 million for fiscal Q3 2026 (quarter ended May 31, 2026), down 9.7% from $235 million a year earlier and down 7.2% from the prior quarter.
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Analysis
Factset Research Systems (FDS) EBITDA (2009 - 2026) Analysis & Trends
For the trailing twelve months through May 31, 2026, EBITDA at Factset Research Systems was $897.08 million, up 5.8% year-over-year; for FY2025 (ended Aug 31, 2025), it was $905.99 million, up 9.6% from FY2024.
- Annual EBITDA has increased for five consecutive fiscal years, with a five-year compound annual growth rate of 12.7% (FY2020 to FY2025).
- In prior fiscal years, Factset Research Systems' EBITDA was $826.49 million in FY2024 (+12.5%), $734.59 million in FY2023 (+30.7%), $562.17 million in FY2022 (+4.4%) and $538.52 million in FY2021 (+8.3%).
- The fiscal Q3 2026 figure stands as the lowest quarterly EBITDA since fiscal Q4 2024.
- On a year-over-year basis, EBITDA increased in six of the last eight quarters, with growth averaging 7.0%.
- The strongest year-over-year quarter for EBITDA in the past five years was fiscal Q3 2023, with growth of 59.3%; the weakest was fiscal Q4 2023, with a decline of 10.0%.
- According to Business Quant data, EBITDA for the three prior fiscal quarters was $228.65 million (Q2 2026), $236.22 million (Q1 2026) and $220.04 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | EBITDA (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.73 Bn | 112.33 Bn | 2.98 Bn | 2.12 Bn |
| 2 | Moodys | 79.69 Bn | 72.12 Bn | 1.67 Bn | 1.17 Bn |
| 3 | Msci | 39.48 Bn | 37.82 Bn | 717.10 Mn | 537.50 Mn |
| 4 | Verisk Analytics | 21.83 Bn | 16.47 Bn | 572.90 Mn | 444.30 Mn |
| 5 | Equifax | 17.14 Bn | 16.54 Bn | 926.40 Mn | 505.60 Mn |
| 6 | TransUnion | 12.68 Bn | 9.66 Bn | - | 418.50 Mn |
| 7 | Factset Research Systems | 9.57 Bn | 8.33 Bn | 310.73 Mn | 212.17 Mn |
| 8 | Morningstar | 7.19 Bn | 5.09 Bn | 423.90 Mn | 214.10 Mn |
| 9 | Mastercard | 494.77 Bn | 454.69 Bn | - | 6.59 Bn |
| 10 | Cme | 94.58 Bn | 94.58 Bn | - | 1.19 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 212.17 Mn |
| Feb 28, 2026 | 228.65 Mn |
| Nov 30, 2025 | 236.22 Mn |
| Aug 31, 2025 | 220.04 Mn |
| May 31, 2025 | 235.00 Mn |
| Feb 28, 2025 | 223.90 Mn |
| Nov 30, 2024 | 227.05 Mn |
| Aug 31, 2024 | 161.89 Mn |
| May 31, 2024 | 234.96 Mn |
| Feb 29, 2024 | 213.52 Mn |
| Nov 30, 2023 | 216.11 Mn |
| Aug 31, 2023 | 142.81 Mn |
| May 31, 2023 | 198.43 Mn |
| Feb 28, 2023 | 195.46 Mn |
| Nov 30, 2022 | 197.89 Mn |
| Aug 31, 2022 | 158.73 Mn |
| May 31, 2022 | 124.60 Mn |
| Feb 28, 2022 | 138.97 Mn |
| Nov 30, 2021 | 139.87 Mn |
| Aug 31, 2021 | 135.47 Mn |
API Access
Factset Research Systems EBITDA 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=ebitda&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ebitda", "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=ebitda&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();