Automatic Data Processing (ADP) Change in Accrued Expenses (2009 - 2026)
Automatic Data Processing (ADP) reported Change in Accrued Expenses of -$389.4 million for fiscal Q1 2027 (quarter ended Sep 30, 2026), compared with -$376.2 million a year earlier.
Automatic Data Processing (ADP) Change in Accrued Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Sep 30, 2026, Automatic Data Processing's Change in Accrued Expenses came in at $396.9 million, up 66.9% year-over-year; for FY2026 (ended Jun 30, 2026), it came in at $410.1 million, up 41.1% from FY2025.
- Change in Accrued Expenses has a five-year compound annual growth rate of 0.8% (FY2021 to FY2026).
- By fiscal year, Change in Accrued 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%).
- The fiscal Q1 2027 figure ranks as the lowest quarterly Change in Accrued Expenses since fiscal Q1 2022.
- Year over year, Change in Accrued Expenses gained in 1 of the last five quarters, with growth averaging 144.5%.
- The high point for year-over-year Change in Accrued 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 Q1 2027 came in at $200 million (Q4 2026), $839.4 million (Q3 2026) and -$253.1 million (Q2 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accrued Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palantir Technologies | 386.60 Bn | 351.42 Bn | 1.93 Bn | 116.14 Mn |
| 2 | Oracle | 343.94 Bn | 216.07 Bn | - | -1.08 Bn |
| 3 | Sap Se | 272.35 Bn | 199.75 Bn | 8.64 Bn | 791.49 Mn |
| 4 | Salesforce | 175.46 Bn | 131.33 Bn | 8.70 Bn | 891.00 Mn |
| 5 | Automatic Data Processing | 122.07 Bn | 108.86 Bn | 2.53 Bn | -389.40 Mn |
| 6 | ServiceNow | 105.47 Bn | 84.73 Bn | 2.90 Bn | 37.00 Mn |
| 7 | Intuit | 94.25 Bn | 73.60 Bn | 3.44 Bn | 304.00 Mn |
| 8 | Relx | 73.66 Bn | 72.75 Bn | - | - |
| 9 | Strategy | 51.78 Bn | 38.91 Bn | 89.63 Mn | -4.08 Mn |
| 10 | Thomson Reuters | 50.52 Bn | 46.49 Bn | - | - |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | -389.40 Mn |
| 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 |
Automatic Data Processing Change in Accrued 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();