Automatic Data Processing (ADP) Price to Earnings (2009 - 2026)
Automatic Data Processing's (ADP) quarterly Price to Earnings came in at 91.04 in Q2 2026, down 33.67% year-over-year from 137.25 in Q2 2025, and up 52.09% quarter-over-quarter from 59.86 in Q1 2026.
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Automatic Data Processing (ADP) Price to Earnings (2009 - 2026) Analysis & Trends
Automatic Data Processing (ADP) has reported Price to Earnings for 18 consecutive years, with 91.04 the latest figure, recorded in Q2 2026.
- On a quarterly basis, Price to Earnings fell 33.67% year-over-year to 91.04 in Q2 2026; TTM through Jun 2026 was 20.19, a 34.12% decrease from a year earlier, with the FY2026 full-year figure at 20.19, down 34.12% from the prior year.
- Price to Earnings was 91.04 for Q2 2026 at Automatic Data Processing, up from 59.86 in the prior quarter.
- Over five years, Price to Earnings peaked at 139.72 in Q2 2022 and troughed at 59.86 in Q1 2026.
- A 5-year average of 108.97 and a median of 115.93 in 2023 frame the typical range for Price to Earnings.
- Across the five-year window, Price to Earnings rose 16.85% in 2025 and tumbled 39.75% in 2026, its largest moves.
- Over 5 years, Price to Earnings stood at 121.74 in 2022, then retreated by 10.52% to 108.93 in 2023, then grew by 13.53% to 123.66 in 2024, then fell by 21.07% to 97.61 in 2025, then declined by 6.74% to 91.04 in 2026.
- The last three Price to Earnings figures came in at 91.04 (Q2 2026), 59.86 (Q1 2026), and 97.61 (Q4 2025), per Business Quant data.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palantir Technologies | 460.85 Bn | 451.55 Bn | 1.64 Bn |
| 2 | Oracle | 437.09 Bn | 400.44 Bn | - |
| 3 | Sap Se | 258.74 Bn | 237.66 Bn | 8.40 Bn |
| 4 | Salesforce | 195.52 Bn | 184.12 Bn | 8.70 Bn |
| 5 | ServiceNow | 145.61 Bn | 140.94 Bn | 2.82 Bn |
| 6 | Automatic Data Processing | 104.89 Bn | 100.66 Bn | 2.51 Bn |
| 7 | Intuit | 76.97 Bn | 69.77 Bn | 3.46 Bn |
| 8 | Relx | 60.41 Bn | 60.04 Bn | - |
| 9 | Strategy | 57.09 Bn | 54.64 Bn | 81.55 Mn |
| 10 | Workday | 46.40 Bn | 42.99 Bn | 2.21 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 20.19 |
| Mar 31, 2026 | 18.73 |
| Dec 31, 2025 | 24.48 |
| Sep 30, 2025 | 28.73 |
| Jun 30, 2025 | 30.64 |
| Mar 31, 2025 | 31.05 |
| Dec 31, 2024 | 30.28 |
| Sep 30, 2024 | 29.31 |
| Jun 30, 2024 | 25.96 |
| Mar 31, 2024 | 27.66 |
| Dec 31, 2023 | 26.89 |
| Sep 30, 2023 | 28.36 |
| Jun 30, 2023 | 26.55 |
| Mar 31, 2023 | 28.23 |
| Dec 31, 2022 | 31.46 |
| Sep 30, 2022 | 31.02 |
| Jun 30, 2022 | 29.64 |
| Mar 31, 2022 | 33.25 |
| Dec 31, 2021 | 37.78 |
| Sep 30, 2021 | 31.28 |
API Access
Automatic Data Processing Price to Earnings 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=price-to-earnings&ticker=ADP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "price-to-earnings", "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=price-to-earnings&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();