Factset Research Systems (FDS) Non Operating Interest Expenses (2022 - 2026)
Factset Research Systems' Non Operating Interest Expenses was $13.84 million in fiscal Q3 2026 (quarter ended May 31, 2026), down 8.5% from $15.12 million a year earlier but up 5.9% from the prior quarter.
Factset Research Systems (FDS) Non Operating Interest Expenses (2022 - 2026) Analysis & Trends
On a trailing twelve-month basis, Factset Research Systems' Non Operating Interest Expenses was $53.17 million through May 31, 2026, down 9.9% year-over-year; for FY2025 (ended Aug 31, 2025), it came in at $56.32 million, down 14.4% from FY2024.
- Non Operating Interest Expenses shows a three-year compound annual growth rate of 16.4% (FY2022 to FY2025).
- In earlier fiscal years, Non Operating Interest Expenses was $65.78 million in FY2024 (-0.8%), $66.32 million in FY2023 (+85.8%) and $35.7 million in FY2022.
- Quarterly Non Operating Interest Expenses has moved between $12.89 million (fiscal Q4 2025) and $16.89 million (fiscal Q3 2024) over five years.
- Compared with a year earlier, Non Operating Interest Expenses has declined for eight straight quarters, with an average decline of 10.8% over the last eight quarters.
- The best year-over-year quarter for Non Operating Interest Expenses over five years was fiscal Q4 2023 (growth of 7.1%); the worst was fiscal Q4 2025 (a decline of 17.1%).
- Per Business Quant data, FDS's Non Operating Interest Expenses in the three fiscal quarters before Q3 2026 was $13.06 million (Q2 2026), $13.39 million (Q1 2026) and $12.89 million (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Non Operating Interest Expenses (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 87.00 Mn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | - |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 71.00 Mn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 52.80 Mn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 59.80 Mn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 65.90 Mn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 13.84 Mn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | - |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 218.00 Mn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 43.60 Mn |
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 13.84 Mn |
| Feb 28, 2026 | 13.06 Mn |
| Nov 30, 2025 | 13.39 Mn |
| Aug 31, 2025 | 12.89 Mn |
| May 31, 2025 | 15.12 Mn |
| Feb 28, 2025 | 13.92 Mn |
| Nov 30, 2024 | 14.40 Mn |
| Aug 31, 2024 | 15.55 Mn |
| May 31, 2024 | 16.89 Mn |
| Feb 29, 2024 | 16.60 Mn |
| Nov 30, 2023 | 16.74 Mn |
| Aug 31, 2023 | 16.69 Mn |
| May 31, 2023 | 16.35 Mn |
| Feb 28, 2023 | 16.74 Mn |
| Nov 30, 2022 | 16.54 Mn |
| Aug 31, 2022 | 15.58 Mn |
| May 31, 2022 | 16.18 Mn |
Factset Research Systems Non Operating Interest 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=non-operating-interest-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "non-operating-interest-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=non-operating-interest-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();