Moodys (MCO) Other Accumulated Expenses (2009 - 2019)
Moodys (MCO) reported Other Accumulated Expenses of $93.4 million for Q2 2019, up 22.3% from $76.4 million a year earlier and up 14.9% from the prior quarter.
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Analysis
Moodys (MCO) Other Accumulated Expenses (2009 - 2019) Analysis & Trends
At the end of FY2018, Moodys posted Other Accumulated Expenses of $95.8 million, up 18.6% from FY2017.
- Other Accumulated Expenses has a five-year compound annual growth rate of 6.2% (FY2013 to FY2018).
- By year, Other Accumulated Expenses came in at $80.8 million in FY2017 (+28.1%), $63.1 million in FY2016 (-10.0%), $70.1 million in FY2015 (+15.9%) and $60.5 million in FY2014 (-14.8%).
- Five-year quarterly Other Accumulated Expenses spans a low of $60.5 million in Q4 2014 and a high of $95.8 million in Q4 2018.
- Year over year, Other Accumulated Expenses has now increased in each of the last four quarters, with growth averaging 11.6% over the last eight quarters.
- The high point for year-over-year Other Accumulated Expenses in five years was Q3 2014 (growth of 37.8%); the low point was Q1 2015 (a decline of 30.6%).
- Per Business Quant data, the three quarters before Q2 2019 came in at $81.3 million (Q1 2019), $95.8 million (Q4 2018) and $91.2 million (Q3 2018).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | S&P Global | 113.90 Bn | 109.50 Bn | 2.98 Bn |
| 2 | Moodys | 76.48 Bn | 68.91 Bn | 1.67 Bn |
| 3 | Msci | 38.99 Bn | 37.33 Bn | 717.10 Mn |
| 4 | Verisk Analytics | 21.33 Bn | 15.96 Bn | 572.90 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 |
| 8 | Morningstar | 6.89 Bn | 4.79 Bn | 423.90 Mn |
| 9 | Mastercard | 480.84 Bn | 440.75 Bn | - |
| 10 | Cme | 94.54 Bn | 94.54 Bn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2019 | 93.40 Mn |
| Mar 31, 2019 | 81.30 Mn |
| Dec 31, 2018 | 95.80 Mn |
| Sep 30, 2018 | 91.20 Mn |
| Jun 30, 2018 | 76.40 Mn |
| Mar 31, 2018 | 78.60 Mn |
| Dec 31, 2017 | 80.80 Mn |
| Sep 30, 2017 | 77.90 Mn |
| Jun 30, 2017 | 81.50 Mn |
| Mar 31, 2017 | 61.30 Mn |
| Dec 31, 2016 | 63.10 Mn |
| Sep 30, 2016 | 95.30 Mn |
| Jun 30, 2016 | 88.70 Mn |
| Mar 31, 2016 | 73.50 Mn |
| Dec 31, 2015 | 70.10 Mn |
| Sep 30, 2015 | 78.50 Mn |
| Jun 30, 2015 | 77.50 Mn |
| Mar 31, 2015 | 66.40 Mn |
| Dec 31, 2014 | 60.50 Mn |
| Sep 30, 2014 | 79.80 Mn |
API Access
Moodys Other 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=other-accumulated-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "other-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=other-accumulated-expenses&ticker=MCO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();