International Business Machines (IBM) Operating Interest Expenses (2009 - 2017)
International Business Machines (IBM) reported Operating Interest Expenses of $320 million for Q4 2017, up 12.7% from $284 million a year earlier but down 0.6% from the prior quarter.
International Business Machines (IBM) Operating Interest Expenses (2009 - 2017) Analysis & Trends
For FY2017, International Business Machines posted Operating Interest Expenses of $1.21 billion, up 15.9% from FY2016.
- Operating Interest Expenses has a five-year compound annual growth rate of 2.2% (FY2012 to FY2017).
- By year, Operating Interest Expenses came in at $1.04 billion in FY2016 (+3.3%), $1.01 billion in FY2015 (-2.8%), $1.04 billion in FY2014 (-6.3%) and $1.11 billion in FY2013 (+2.1%).
- Five-year quarterly Operating Interest Expenses spans a low of $230 million in Q2 2014 and a high of $322 million in Q3 2017.
- Year over year, Operating Interest Expenses has now increased in each of the last six quarters, with growth averaging 9.7% over the last eight quarters.
- The high point for year-over-year Operating Interest Expenses in five years was Q3 2017 (growth of 24.3%); the low point was Q1 2015 (a decline of 15.5%).
- Per Business Quant data, the three quarters before Q4 2017 came in at $322 million (Q3 2017), $289 million (Q2 2017) and $279 million (Q1 2017).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Op. Interest Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Microsoft | 3,843.70 Bn | 3,766.85 Bn | 60.48 Bn | - |
| 2 | International Business Machines | 209.74 Bn | 160.64 Bn | 9.91 Bn | - |
| 3 | Cloudflare | 112.41 Bn | 95.94 Bn | 499.52 Mn | - |
| 4 | Equinix | 101.21 Bn | 89.76 Bn | 1.40 Bn | - |
| 5 | Nebius | 61.44 Bn | 34.87 Bn | 448.70 Mn | - |
| 6 | CoreWeave | 49.38 Bn | 36.47 Bn | 1.70 Bn | - |
| 7 | Verisign | 26.06 Bn | 23.28 Bn | 384.60 Mn | - |
| 8 | Nutanix | 19.56 Bn | 11.24 Bn | 651.35 Mn | - |
| 9 | Akamai Technologies | 15.64 Bn | 9.05 Bn | 613.75 Mn | - |
| 10 | DigitalOcean Holdings | 14.71 Bn | 12.71 Bn | 154.66 Mn | - |
Historic Data
| Date | Value |
|---|---|
| Dec 31, 2017 | 320.00 Mn |
| Sep 30, 2017 | 322.00 Mn |
| Jun 30, 2017 | 289.00 Mn |
| Mar 31, 2017 | 279.00 Mn |
| Dec 31, 2016 | 284.00 Mn |
| Sep 30, 2016 | 259.00 Mn |
| Jun 30, 2016 | 263.00 Mn |
| Mar 31, 2016 | 238.00 Mn |
| Dec 31, 2015 | 278.00 Mn |
| Sep 30, 2015 | 233.00 Mn |
| Jun 30, 2015 | 266.00 Mn |
| Mar 31, 2015 | 235.00 Mn |
| Dec 31, 2014 | 275.00 Mn |
| Sep 30, 2014 | 257.00 Mn |
| Jun 30, 2014 | 230.00 Mn |
| Mar 31, 2014 | 278.00 Mn |
| Dec 31, 2013 | 305.00 Mn |
| Sep 30, 2013 | 268.00 Mn |
| Jun 30, 2013 | 264.00 Mn |
| Mar 31, 2013 | 273.00 Mn |
International Business Machines Operating Interest 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=operating-interest-expenses&ticker=IBM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-interest-expenses", "ticker": "IBM", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=operating-interest-expenses&ticker=IBM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();