Factset Research Systems (FDS) Operating Expenses (2009 - 2026)
Factset Research Systems (FDS) posted Operating Expenses of $456.62 million for fiscal Q3 2026 (quarter ended May 31, 2026), up 16.7% from $391.37 million a year earlier and up 7.2% from the prior quarter.
Factset Research Systems (FDS) Operating Expenses (2009 - 2026) Analysis & Trends
For the trailing twelve months through May 31, 2026, Operating Expenses at Factset Research Systems was $1.72 billion, up 8.2% year-over-year; for FY2025 (ended Aug 31, 2025), it came in at $1.57 billion, up 4.8% from FY2024.
- Annual Operating Expenses has increased for 16 consecutive fiscal years, with a five-year compound annual growth rate of 8.3% (FY2020 to FY2025).
- In prior fiscal years, Factset Research Systems' Operating Expenses was $1.5 billion in FY2024 (+3.1%), $1.46 billion in FY2023 (+6.4%), $1.37 billion in FY2022 (+22.5%) and $1.12 billion in FY2021 (+6.0%).
- The fiscal Q3 2026 figure stands as the highest quarterly Operating Expenses in data going back to fiscal Q1 2010.
- On a year-over-year basis, Operating Expenses has increased in each of the last three quarters, with growth averaging 7.7% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was fiscal Q3 2022, with growth of 38.9%; the weakest was fiscal Q3 2023, with a decline of 8.6%.
- According to Business Quant data, Operating Expenses for the three prior fiscal quarters was $426.06 million (Q2 2026), $415.55 million (Q1 2026) and $419.58 million (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 2.35 Bn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 1.14 Bn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 379.50 Mn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 442.60 Mn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 1.39 Bn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 1.05 Bn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 456.62 Mn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 509.40 Mn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 3.69 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 599.10 Mn |
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 456.62 Mn |
| Feb 28, 2026 | 426.06 Mn |
| Nov 30, 2025 | 415.55 Mn |
| Aug 31, 2025 | 419.58 Mn |
| May 31, 2025 | 391.37 Mn |
| Feb 28, 2025 | 385.17 Mn |
| Nov 30, 2024 | 377.33 Mn |
| Aug 31, 2024 | 434.33 Mn |
| May 31, 2024 | 350.25 Mn |
| Feb 29, 2024 | 364.00 Mn |
| Nov 30, 2023 | 353.18 Mn |
| Aug 31, 2023 | 419.69 Mn |
| May 31, 2023 | 357.85 Mn |
| Feb 28, 2023 | 345.84 Mn |
| Nov 30, 2022 | 332.92 Mn |
| Aug 31, 2022 | 367.08 Mn |
| May 31, 2022 | 391.50 Mn |
| Feb 28, 2022 | 307.77 Mn |
| Nov 30, 2021 | 302.06 Mn |
| Aug 31, 2021 | 292.72 Mn |
Factset Research Systems Operating 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=operating-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-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=operating-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();