Factset Research Systems (FDS) Interest Expenses (2022 - 2026)
Factset Research Systems (FDS) reported Interest Expenses of $13.84 million for 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) Interest Expenses (2022 - 2026) Analysis & Trends
Over the twelve months ended May 31, 2026, Factset Research Systems' Interest Expenses came in at $53.17 million, down 9.9% year-over-year; for FY2025 (ended Aug 31, 2025), it was $56.32 million, down 14.4% from FY2024.
- Interest Expenses has a three-year compound annual growth rate of 16.4% (FY2022 to FY2025).
- By fiscal year, Interest Expenses came in at $65.78 million in FY2024 (-0.8%), $66.32 million in FY2023 (+85.8%) and $35.7 million in FY2022.
- Five-year quarterly Interest Expenses spans a low of $12.89 million in fiscal Q4 2025 and a high of $16.89 million in fiscal Q3 2024.
- Year over year, Interest Expenses has now declined in each of the last eight quarters, with an average decline of 10.8% over the last eight quarters.
- The high point for year-over-year Interest Expenses in five years was fiscal Q4 2023 (growth of 7.1%); the low point was fiscal Q4 2025 (a decline of 17.1%).
- Per Business Quant data, the three fiscal quarters before Q3 2026 came in at $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) | Int Expense (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 115.73 Bn | 111.34 Bn | 2.98 Bn | 87.00 Mn |
| 2 | Moodys | 78.77 Bn | 71.20 Bn | 1.67 Bn | - |
| 3 | Msci | 39.23 Bn | 37.58 Bn | 717.10 Mn | 71.00 Mn |
| 4 | Verisk Analytics | 21.59 Bn | 16.23 Bn | 572.90 Mn | 52.80 Mn |
| 5 | Equifax | 16.56 Bn | 15.96 Bn | 926.40 Mn | 59.80 Mn |
| 6 | TransUnion | 12.32 Bn | 9.30 Bn | - | 65.90 Mn |
| 7 | Factset Research Systems | 9.30 Bn | 8.06 Bn | 310.73 Mn | 13.84 Mn |
| 8 | Morningstar | 6.99 Bn | 4.89 Bn | 423.90 Mn | - |
| 9 | Mastercard | 490.78 Bn | 450.70 Bn | - | 218.00 Mn |
| 10 | Cme | 94.57 Bn | 94.57 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 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=interest-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "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=interest-expenses&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();