Moodys (MCO) Change in Accured Expenses (2009 - 2026)
Moodys' Change in Accured Expenses came in at -$18 million for Q2 2026, compared with -$49 million a year earlier.
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Analysis
Moodys (MCO) Change in Accured Expenses (2009 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Moodys reported Change in Accured Expenses of $93 million; for FY2025, it was -$55 million.
- Going back by year, Change in Accured Expenses was $225 million in FY2024 (+196.1%), $76 million in FY2023, -$161 million in FY2022 and $80 million in FY2021 (-67.6%).
- The five-year range for quarterly Change in Accured Expenses is -$296 million (Q1 2022) to $262 million (Q4 2025).
- Year-over-year, Change in Accured Expenses increased in three of the last four quarters, with growth averaging 101.0%.
- The fastest year-over-year change in Change in Accured Expenses over five years came in Q3 2024 (growth of 408.0%), and the weakest in Q3 2025 (a decline of 81.1%).
- Business Quant data shows MCO's Change in Accured Expenses at -$175 million (Q1 2026), $262 million (Q4 2025) and $24 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 113.90 Bn | 109.50 Bn | 2.98 Bn | 243.00 Mn |
| 2 | Moodys | 76.48 Bn | 68.91 Bn | 1.67 Bn | -18.00 Mn |
| 3 | Msci | 38.99 Bn | 37.33 Bn | 717.10 Mn | 61.50 Mn |
| 4 | Verisk Analytics | 21.33 Bn | 15.96 Bn | 572.90 Mn | 59.70 Mn |
| 5 | Equifax | 16.47 Bn | 15.87 Bn | 926.40 Mn | - |
| 6 | TransUnion | 12.16 Bn | 9.14 Bn | - | - |
| 7 | Factset Research Systems | 9.52 Bn | 8.28 Bn | 310.73 Mn | 65.03 Mn |
| 8 | Morningstar | 6.89 Bn | 4.79 Bn | 423.90 Mn | 40.60 Mn |
| 9 | Mastercard | 480.84 Bn | 440.75 Bn | - | 749.00 Mn |
| 10 | Cme | 94.54 Bn | 94.54 Bn | - | -70.30 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | -18.00 Mn |
| Mar 31, 2026 | -175.00 Mn |
| Dec 31, 2025 | 262.00 Mn |
| Sep 30, 2025 | 24.00 Mn |
| Jun 30, 2025 | -49.00 Mn |
| Mar 31, 2025 | -292.00 Mn |
| Dec 31, 2024 | 180.00 Mn |
| Sep 30, 2024 | 127.00 Mn |
| Jun 30, 2024 | 28.00 Mn |
| Mar 31, 2024 | -110.00 Mn |
| Dec 31, 2023 | 137.00 Mn |
| Sep 30, 2023 | 25.00 Mn |
| Jun 30, 2023 | 92.00 Mn |
| Mar 31, 2023 | -178.00 Mn |
| Dec 31, 2022 | 197.00 Mn |
| Sep 30, 2022 | -82.00 Mn |
| Jun 30, 2022 | 20.00 Mn |
| Mar 31, 2022 | -296.00 Mn |
| Dec 31, 2021 | 90.00 Mn |
| Sep 30, 2021 | 165.00 Mn |
API Access
Moodys Change in Accured 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=change-in-accured-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "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=change-in-accured-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();