Moodys (MCO) Accumulated Expenses (2009 - 2019)
Moodys (MCO) posted Accumulated Expenses of $111.9 million for Q2 2019, up 44.0% from $77.7 million a year earlier and up 56.9% from the prior quarter.
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Analysis
Moodys (MCO) Accumulated Expenses (2009 - 2019) Analysis & Trends
At the end of FY2018, Moodys' Accumulated Expenses came in at $116.6 million, up 41.5% from FY2017.
- Annual Accumulated Expenses shows a five-year compound annual growth rate of 33.4% (FY2013 to FY2018).
- In prior years, Moodys' Accumulated Expenses was $82.4 million in FY2017 (+7.6%), $76.6 million in FY2016 (-3.2%), $79.1 million in FY2015 (+18.9%) and $66.5 million in FY2014 (+140.9%).
- Quarterly Accumulated Expenses has run from a low of $31.6 million in Q1 2017 to a high of $116.6 million in Q4 2018 over five years.
- On a year-over-year basis, Accumulated Expenses has increased in each of the last seven quarters, with growth averaging 26.4% over the last eight quarters.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was Q4 2014, with growth of 140.9%; the weakest was Q2 2017, with a decline of 22.0%.
- According to Business Quant data, Accumulated Expenses for the three prior quarters was $71.3 million (Q1 2019), $116.6 million (Q4 2018) and $49.3 million (Q3 2018).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Msci | 236.00 Bn | 234.48 Bn | 739.80 Mn |
| 2 | S&P Global | 137.30 Bn | 132.50 Bn | 2.64 Bn |
| 3 | Moodys | 87.63 Bn | 80.69 Bn | 1.50 Bn |
| 4 | Verisk Analytics | 26.66 Bn | 22.85 Bn | 567.00 Mn |
| 5 | Equifax | 22.03 Bn | 21.43 Bn | 940.10 Mn |
| 6 | TransUnion | 15.46 Bn | 12.30 Bn | - |
| 7 | Factset Research Systems | 11.73 Bn | 10.58 Bn | 308.40 Mn |
| 8 | Morningstar | 9.14 Bn | 7.03 Bn | 431.40 Mn |
| 9 | Mastercard | 572.29 Bn | 532.37 Bn | - |
| 10 | Cme | 113.65 Bn | 101.99 Bn | 359.20 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2019 | 111.90 Mn |
| Mar 31, 2019 | 71.30 Mn |
| Dec 31, 2018 | 116.60 Mn |
| Sep 30, 2018 | 49.30 Mn |
| Jun 30, 2018 | 77.70 Mn |
| Mar 31, 2018 | 40.10 Mn |
| Dec 31, 2017 | 82.40 Mn |
| Sep 30, 2017 | 47.90 Mn |
| Jun 30, 2017 | 63.30 Mn |
| Mar 31, 2017 | 31.60 Mn |
| Dec 31, 2016 | 76.60 Mn |
| Sep 30, 2016 | 54.40 Mn |
| Jun 30, 2016 | 81.20 Mn |
| Mar 31, 2016 | 37.00 Mn |
| Dec 31, 2015 | 79.10 Mn |
| Sep 30, 2015 | 44.50 Mn |
| Jun 30, 2015 | 77.10 Mn |
| Mar 31, 2015 | 37.90 Mn |
| Dec 31, 2014 | 66.50 Mn |
| Sep 30, 2014 | 33.90 Mn |
API Access
Moodys Accumulated 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=accumulated-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-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=accumulated-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();