GXO Logistics (GXO) Accumulated Expenses (2020 - 2026)
GXO Logistics (GXO) posted Accumulated Expenses of $1.4 billion for Q2 2026, up 0.56% on a QoQ basis from $1.4 billion in Q1 2026, and up 4.63% year-over-year from $1.4 billion in Q2 2025.
GXO Logistics (GXO) Accumulated Expenses (2020 - 2026) Analysis & Trends
GXO Logistics has reported Accumulated Expenses for 7 years, with the latest figure at $1.4 billion in Q2 2026.
- On a quarterly basis, Accumulated Expenses rose 4.63% year-over-year to $1.4 billion in Q2 2026; TTM through Jun 2026 was $1.4 billion, a 4.63% increase from a year earlier, with the FY2025 full-year figure at $1.5 billion, up 17.39% from the prior year.
- Accumulated Expenses was $1.4 billion for Q2 2026 at GXO Logistics, up from $1.4 billion in the prior quarter.
- The five-year high for Accumulated Expenses was $1.5 billion in Q3 2025, with the low at $908.0 million in Q1 2023.
- Average Accumulated Expenses over 5 years is $1.2 billion, with a median of $1.1 billion recorded in 2022.
- The sharpest annual moves came in 2023 and 2024: Accumulated Expenses declined 6.13% in 2023, then surged 44.72% in 2024.
- Over 5 years, Accumulated Expenses stood at $995.0 million in 2022, then fell by 2.91% to $966.0 million in 2023, then surged by 31.57% to $1.3 billion in 2024, then climbed by 17.39% to $1.5 billion in 2025, then declined by 3.15% to $1.4 billion in 2026.
- The last three Accumulated Expenses figures came in at $1.4 billion (Q2 2026), $1.4 billion (Q1 2026), and $1.5 billion (Q4 2025), per Business Quant data.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Copart | 26.66 Bn | 23.33 Bn | 732.88 Mn |
| 2 | Expeditors International Of Washington | 24.38 Bn | 23.35 Bn | 1.09 Bn |
| 3 | C. H. Robinson Worldwide | 17.52 Bn | 17.36 Bn | 1.11 Bn |
| 4 | Rb Global | 15.43 Bn | 14.90 Bn | 935.50 Mn |
| 5 | Ryder System | 9.07 Bn | 9.07 Bn | 1.45 Bn |
| 6 | Landstar System | 5.72 Bn | 5.38 Bn | 308.86 Mn |
| 7 | GXO Logistics | 5.25 Bn | 4.52 Bn | 508.00 Mn |
| 8 | Rxo | 3.26 Bn | 3.25 Bn | 302.00 Mn |
| 9 | Hub | 1.94 Bn | 1.85 Bn | 250.84 Mn |
| 10 | Cryoport | 889.89 Mn | 493.20 Mn | 22.82 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.45 Bn |
| Mar 31, 2026 | 1.44 Bn |
| Dec 31, 2025 | 1.49 Bn |
| Sep 30, 2025 | 1.52 Bn |
| Jun 30, 2025 | 1.38 Bn |
| Mar 31, 2025 | 1.40 Bn |
| Dec 31, 2024 | 1.27 Bn |
| Sep 30, 2024 | 1.41 Bn |
| Jun 30, 2024 | 1.29 Bn |
| Mar 31, 2024 | 976.00 Mn |
| Dec 31, 2023 | 966.00 Mn |
| Sep 30, 2023 | 975.00 Mn |
| Jun 30, 2023 | 950.00 Mn |
| Mar 31, 2023 | 908.00 Mn |
| Dec 31, 2022 | 995.00 Mn |
| Sep 30, 2022 | 952.00 Mn |
| Jun 30, 2022 | 1.01 Bn |
| Mar 31, 2022 | 940.00 Mn |
| Dec 31, 2021 | 998.00 Mn |
| Sep 30, 2021 | 1.01 Bn |
GXO Logistics 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=GXO&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "GXO", "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=GXO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();