Moodys (MCO) Total Liabilities (2009 - 2026)
Moodys (MCO) posted Total Liabilities of $11.51 billion for Q2 2026, up 1.1% from $11.38 billion a year earlier but down 0.7% from the prior quarter.
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Analysis
Moodys (MCO) Total Liabilities (2009 - 2026) Analysis & Trends
At the end of FY2025, Moodys' Total Liabilities came in at $11.63 billion, down 1.3% from FY2024.
- Annual Total Liabilities shows a five-year compound annual growth rate of 1.8% (FY2020 to FY2025).
- In prior years, Moodys' Total Liabilities was $11.78 billion in FY2024 (+5.7%), $11.15 billion in FY2023 (-4.4%), $11.66 billion in FY2022 (-0.9%) and $11.76 billion in FY2021 (+10.5%).
- Quarterly Total Liabilities has run from a low of $10.84 billion in Q3 2023 to a high of $12.16 billion in Q1 2022 over five years.
- On a year-over-year basis, Total Liabilities increased in five of the last eight quarters, with growth averaging 1.9%.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q1 2022, with growth of 18.3%; the weakest was Q3 2023, with a decline of 5.8%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $11.59 billion (Q1 2026), $11.63 billion (Q4 2025) and $11.3 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 31.29 Bn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 11.51 Bn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 8.29 Bn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 5.68 Bn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 7.58 Bn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 7.19 Bn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 2.16 Bn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 2.92 Bn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 52.08 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 168.16 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 11.51 Bn |
| Mar 31, 2026 | 11.59 Bn |
| Dec 31, 2025 | 11.63 Bn |
| Sep 30, 2025 | 11.30 Bn |
| Jun 30, 2025 | 11.38 Bn |
| Mar 31, 2025 | 11.24 Bn |
| Dec 31, 2024 | 11.78 Bn |
| Sep 30, 2024 | 11.70 Bn |
| Jun 30, 2024 | 11.08 Bn |
| Mar 31, 2024 | 11.28 Bn |
| Dec 31, 2023 | 11.15 Bn |
| Sep 30, 2023 | 10.84 Bn |
| Jun 30, 2023 | 11.38 Bn |
| Mar 31, 2023 | 11.66 Bn |
| Dec 31, 2022 | 11.66 Bn |
| Sep 30, 2022 | 11.51 Bn |
| Jun 30, 2022 | 11.84 Bn |
| Mar 31, 2022 | 12.16 Bn |
| Dec 31, 2021 | 11.76 Bn |
| Sep 30, 2021 | 11.80 Bn |
API Access
Moodys Total Liabilities 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=total-liabilities&ticker=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "MCO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();