Automatic Data Processing (ADP) Change in Accured Expenses (2009 - 2026)
Automatic Data Processing (ADP) reported Change in Accured Expenses of $200 million for fiscal Q4 2026 (quarter ended Jun 30, 2026), down 35.8% from $311.5 million a year earlier and down 76.2% from the prior quarter.
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Analysis
Automatic Data Processing (ADP) Change in Accured Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended Jun 30, 2026), Automatic Data Processing posted Change in Accured Expenses of $410.1 million, up 41.1% from FY2025.
- Change in Accured Expenses has a five-year compound annual growth rate of 0.8% (FY2021 to FY2026).
- By fiscal year, Change in Accured Expenses came in at $290.6 million in FY2025 (-41.0%), $492.9 million in FY2024 (+63.6%), $301.3 million in FY2023 (+145.0%) and $123 million in FY2022 (-68.9%).
- Five-year quarterly Change in Accured Expenses spans a low of -$413.8 million in fiscal Q1 2022 and a high of $839.4 million in fiscal Q3 2026.
- Year over year, Change in Accured Expenses gained in 1 of the last five quarters, with growth averaging 144.5%.
- The high point for year-over-year Change in Accured Expenses in five years was fiscal Q3 2026 (growth of 836.8%); the low point was fiscal Q3 2023 (a decline of 88.6%).
- Per Business Quant data, the three fiscal quarters before Q4 2026 came in at $839.4 million (Q3 2026), -$253.1 million (Q2 2026) and -$376.2 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palantir Technologies | 480.73 Bn | 449.68 Bn | 1.64 Bn | 7.31 Mn |
| 2 | Oracle | 399.91 Bn | 272.04 Bn | - | -1.08 Bn |
| 3 | Sap Se | 262.30 Bn | 190.08 Bn | 8.40 Bn | -354.52 Mn |
| 4 | Salesforce | 202.26 Bn | 158.13 Bn | 8.70 Bn | 891.00 Mn |
| 5 | ServiceNow | 153.84 Bn | 132.30 Bn | 2.82 Bn | 280.00 Mn |
| 6 | Automatic Data Processing | 114.84 Bn | 96.97 Bn | 2.51 Bn | 200.00 Mn |
| 7 | Intuit | 91.04 Bn | 70.38 Bn | 3.44 Bn | 304.00 Mn |
| 8 | Relx | 70.51 Bn | 69.60 Bn | - | - |
| 9 | Strategy | 53.24 Bn | 46.23 Bn | 81.55 Mn | -2.04 Mn |
| 10 | Thomson Reuters | 48.40 Bn | 45.18 Bn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 200.00 Mn |
| Mar 31, 2026 | 839.40 Mn |
| Dec 31, 2025 | -253.10 Mn |
| Sep 30, 2025 | -376.20 Mn |
| Jun 30, 2025 | 311.50 Mn |
| Mar 31, 2025 | 89.60 Mn |
| Dec 31, 2024 | 212.90 Mn |
| Sep 30, 2024 | -323.40 Mn |
| Jun 30, 2024 | 353.70 Mn |
| Mar 31, 2024 | 216.30 Mn |
| Dec 31, 2023 | 231.50 Mn |
| Sep 30, 2023 | -308.60 Mn |
| Jun 30, 2023 | 326.90 Mn |
| Mar 31, 2023 | 53.10 Mn |
| Dec 31, 2022 | 116.00 Mn |
| Sep 30, 2022 | -194.70 Mn |
| Jun 30, 2022 | 19.30 Mn |
| Mar 31, 2022 | 467.00 Mn |
| Dec 31, 2021 | 50.50 Mn |
| Sep 30, 2021 | -413.80 Mn |
API Access
Automatic Data Processing Change in Accured 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=change-in-accured-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "ADP", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();