International Business Machines (IBM) Accumulated Expenses (2014 - 2026)
International Business Machines (IBM) posted Accumulated Expenses of $3.43 billion for Q2 2026, down 12.9% from $3.93 billion a year earlier and down 4.4% from the prior quarter.
International Business Machines (IBM) Accumulated Expenses (2014 - 2026) Analysis & Trends
At the end of FY2025, International Business Machines' Accumulated Expenses came in at $4.12 billion, up 11.0% from FY2024.
- Annual Accumulated Expenses shows a five-year compound annual growth rate of -6.1% (FY2020 to FY2025).
- In prior years, International Business Machines' Accumulated Expenses was $3.71 billion in FY2024 (+5.3%), $3.52 billion in FY2023 (-14.4%), $4.11 billion in FY2022 (+5.6%) and $3.89 billion in FY2021 (-30.9%).
- The Q2 2026 figure stands as the lowest quarterly Accumulated Expenses since Q2 2024.
- On a year-over-year basis, Accumulated Expenses increased in six of the last eight quarters, with growth averaging 3.9%.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was Q2 2025, with growth of 23.1%; the weakest was Q1 2022, with a decline of 35.9%.
- According to Business Quant data, Accumulated Expenses for the three prior quarters was $3.58 billion (Q1 2026), $4.12 billion (Q4 2025) and $3.48 billion (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Microsoft | 3,892.50 Bn | 3,815.66 Bn | 60.48 Bn |
| 2 | International Business Machines | 238.73 Bn | 189.54 Bn | 9.91 Bn |
| 3 | Cloudflare | 114.72 Bn | 98.25 Bn | 499.52 Mn |
| 4 | Equinix | 94.20 Bn | 82.76 Bn | 1.40 Bn |
| 5 | Nebius | 38.48 Bn | 11.91 Bn | 448.70 Mn |
| 6 | CoreWeave | 29.87 Bn | 16.96 Bn | 1.70 Bn |
| 7 | Verisign | 27.08 Bn | 24.44 Bn | 393.20 Mn |
| 8 | Nutanix | 18.92 Bn | 10.61 Bn | 651.35 Mn |
| 9 | GoDaddy | 14.68 Bn | 10.25 Bn | 828.20 Mn |
| 10 | Akamai Technologies | 13.49 Bn | 6.90 Bn | 613.75 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 3.43 Bn |
| Mar 31, 2026 | 3.58 Bn |
| Dec 31, 2025 | 4.12 Bn |
| Sep 30, 2025 | 3.48 Bn |
| Jun 30, 2025 | 3.93 Bn |
| Mar 31, 2025 | 3.53 Bn |
| Dec 31, 2024 | 3.71 Bn |
| Sep 30, 2024 | 3.47 Bn |
| Jun 30, 2024 | 3.20 Bn |
| Mar 31, 2024 | 3.61 Bn |
| Dec 31, 2023 | 3.52 Bn |
| Sep 30, 2023 | 3.31 Bn |
| Jun 30, 2023 | 3.65 Bn |
| Mar 31, 2023 | 3.87 Bn |
| Dec 31, 2022 | 4.11 Bn |
| Sep 30, 2022 | 3.70 Bn |
| Jun 30, 2022 | 3.68 Bn |
| Mar 31, 2022 | 3.70 Bn |
| Dec 31, 2021 | 3.89 Bn |
| Sep 30, 2021 | 4.52 Bn |
International Business Machines 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=IBM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-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=accumulated-expenses&ticker=IBM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();