Factset Research Systems (FDS) Non Operating Interest Expenses (2022 - 2026)
Factset Research Systems' Non Operating Interest Expenses was $14.4 million in fiscal Q4 2026 (quarter ended Aug 31, 2026), up 11.7% from $12.89 million a year earlier and up 4.0% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Non Operating Interest Expenses (2022 - 2026) Analysis & Trends
For FY2026 (ended Aug 31, 2026), Non Operating Interest Expenses at Factset Research Systems came in at $54.68 million, down 2.9% from FY2025.
- Non Operating Interest Expenses has now declined for three consecutive fiscal years, though with a four-year compound annual growth rate of 11.3% (FY2022 to FY2026).
- In earlier fiscal years, Non Operating Interest Expenses was $56.32 million in FY2025 (-14.4%), $65.78 million in FY2024 (-0.8%), $66.32 million in FY2023 (+85.8%) and $35.7 million in FY2022.
- The fiscal Q4 2026 figure marks the highest quarterly Non Operating Interest Expenses since fiscal Q3 2025.
- Compared with a year earlier, Non Operating Interest Expenses was higher in 1 of the last eight quarters, with an average decline of 8.5%.
- The best year-over-year quarter for Non Operating Interest Expenses over five years was fiscal Q4 2026 (growth of 11.7%); 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 Q4 2026 was $13.84 million (Q3 2026), $13.06 million (Q2 2026) and $13.39 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Non Operating Interest Expenses (Qtr) |
|---|---|---|---|---|---|
| 1 | Msci | 235.55 Bn | 234.03 Bn | 739.80 Mn | 74.80 Mn |
| 2 | S&P Global | 137.22 Bn | 132.42 Bn | 2.64 Bn | 87.00 Mn |
| 3 | Moodys | 88.04 Bn | 81.11 Bn | 1.50 Bn | - |
| 4 | Verisk Analytics | 26.17 Bn | 22.36 Bn | 567.00 Mn | 53.00 Mn |
| 5 | Equifax | 22.05 Bn | 21.44 Bn | 940.10 Mn | 62.30 Mn |
| 6 | TransUnion | 15.64 Bn | 12.48 Bn | - | 66.50 Mn |
| 7 | Factset Research Systems | 11.49 Bn | 10.35 Bn | 308.40 Mn | 14.40 Mn |
| 8 | Morningstar | 9.15 Bn | 7.04 Bn | 431.40 Mn | - |
| 9 | Mastercard | 570.55 Bn | 530.63 Bn | - | 250.00 Mn |
| 10 | Cme | 110.48 Bn | 98.81 Bn | 359.20 Mn | 43.50 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 14.40 Mn |
| 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 |
API Access
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();