Factset Research Systems (FDS) Cash & Equivalents (2009 - 2026)
Factset Research Systems (FDS) posted Cash & Equivalents of $233.34 million for fiscal Q4 2026 (quarter ended Aug 31, 2026), down 30.9% from $337.65 million a year earlier and down 19.0% from the prior quarter.
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Analysis
Factset Research Systems (FDS) Cash & Equivalents (2009 - 2026) Analysis & Trends
Since fiscal Q1 2010, Factset Research Systems has reported Cash & Equivalents for 68 quarters.
- Annual Cash & Equivalents has declined for five consecutive fiscal years, with a five-year compound annual growth rate of -19.3% (FY2021 to FY2026).
- In prior fiscal years, Factset Research Systems' Cash & Equivalents was $337.65 million in FY2025 (-20.2%), $422.98 million in FY2024 (-0.6%), $425.44 million in FY2023 (-15.5%) and $503.27 million in FY2022 (-26.2%).
- The fiscal Q4 2026 figure stands as the lowest quarterly Cash & Equivalents since fiscal Q2 2019.
- On a year-over-year basis, Cash & Equivalents has declined in each of the last 18 quarters, with an average decline of 19.6% over the last eight quarters.
- The strongest year-over-year quarter for Cash & Equivalents in the past five years was fiscal Q2 2022, with growth of 28.3%; the weakest was fiscal Q2 2023, with a decline of 42.4%.
- According to Business Quant data, Cash & Equivalents for the three prior fiscal quarters was $288.11 million (Q3 2026), $268.34 million (Q2 2026) and $275.45 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Cash & Equiv. (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 132.72 Bn | 128.33 Bn | 2.98 Bn | 4.13 Bn |
| 2 | Moodys | 83.35 Bn | 76.42 Bn | 1.50 Bn | 1.51 Bn |
| 3 | Msci | 44.14 Bn | 42.62 Bn | 739.80 Mn | 261.00 Mn |
| 4 | Verisk Analytics | 25.49 Bn | 20.13 Bn | 572.90 Mn | 551.40 Mn |
| 5 | Equifax | 21.35 Bn | 20.74 Bn | 940.10 Mn | 192.90 Mn |
| 6 | TransUnion | 15.61 Bn | 12.59 Bn | - | 839.10 Mn |
| 7 | Factset Research Systems | 11.68 Bn | 10.53 Bn | 308.40 Mn | 233.34 Mn |
| 8 | Morningstar | 8.44 Bn | 6.34 Bn | 423.90 Mn | 489.50 Mn |
| 9 | Mastercard | 542.86 Bn | 502.77 Bn | - | 11.29 Bn |
| 10 | Cme | 109.27 Bn | 97.61 Bn | - | 2.20 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Aug 31, 2026 | 233.34 Mn |
| May 31, 2026 | 288.11 Mn |
| Feb 28, 2026 | 268.34 Mn |
| Nov 30, 2025 | 275.45 Mn |
| Aug 31, 2025 | 337.65 Mn |
| May 31, 2025 | 356.36 Mn |
| Feb 28, 2025 | 278.55 Mn |
| Nov 30, 2024 | 289.17 Mn |
| Aug 31, 2024 | 422.98 Mn |
| May 31, 2024 | 453.14 Mn |
| Feb 29, 2024 | 381.71 Mn |
| Nov 30, 2023 | 411.86 Mn |
| Aug 31, 2023 | 425.44 Mn |
| May 31, 2023 | 486.63 Mn |
| Feb 28, 2023 | 445.33 Mn |
| Nov 30, 2022 | 437.14 Mn |
| Aug 31, 2022 | 503.27 Mn |
| May 31, 2022 | 526.97 Mn |
| Feb 28, 2022 | 773.01 Mn |
| Nov 30, 2021 | 673.90 Mn |
API Access
Factset Research Systems Cash & Equivalents 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=cash-and-equivalents&ticker=FDS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "cash-and-equivalents", "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=cash-and-equivalents&ticker=FDS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();