AUTOMATIC DATA PROCESSING, INC.: revenue

What this measure means

Revenue under this specific accounting concept. A missing value is not zero; filers can use other revenue concepts.

Exact concept: us-gaap:Revenues. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2007-07-01 to 2026-06-30. The SEC response was captured on 2026-09-19.

Selected filing history

Related financial histories

• AUTOMATIC DATA PROCESSING, INC.: total assets
• AUTOMATIC DATA PROCESSING, INC.: total liabilities
• AUTOMATIC DATA PROCESSING, INC.: stockholders equity
• AUTOMATIC DATA PROCESSING, INC.: cash and cash equivalents
• AUTOMATIC DATA PROCESSING, INC.: net income or loss
• AUTOMATIC DATA PROCESSING, INC.: operating cash flow
• AUTOMATIC DATA PROCESSING, INC.: contract revenue excluding tax
• AUTOMATIC DATA PROCESSING, INC.: financing cash flow
• AUTOMATIC DATA PROCESSING, INC.: investing cash flow
• AUTOMATIC DATA PROCESSING, INC.: retained earnings or deficit
• AUTOMATIC DATA PROCESSING, INC.: basic weighted-average shares
• AUTOMATIC DATA PROCESSING, INC.: diluted weighted-average shares
• AUTOMATIC DATA PROCESSING, INC.: basic earnings per share
• AUTOMATIC DATA PROCESSING, INC.: diluted earnings per share
• AUTOMATIC DATA PROCESSING, INC.: income tax expense or benefit
• AUTOMATIC DATA PROCESSING, INC.: net property, plant and equipment
• AUTOMATIC DATA PROCESSING, INC.: share-based compensation expense
• AUTOMATIC DATA PROCESSING, INC.: current assets
• AUTOMATIC DATA PROCESSING, INC.: interest expense
• AUTOMATIC DATA PROCESSING, INC.: current liabilities
• AUTOMATIC DATA PROCESSING, INC.: current accounts payable
• AUTOMATIC DATA PROCESSING, INC.: goodwill carrying amount
• AUTOMATIC DATA PROCESSING, INC.: net current accounts receivable
• AUTOMATIC DATA PROCESSING, INC.: common-stock repurchase payments
• AUTOMATIC DATA PROCESSING, INC.: operating expenses
• AUTOMATIC DATA PROCESSING, INC.: selling, general and administrative expense
• AUTOMATIC DATA PROCESSING, INC.: research and development expense

Inspect the source

Entity

AUTOMATIC DATA PROCESSING, INC. / CIK 0000008670

Captured

2026-09-19T14:43:41.606Z

SEC response SHA-256

84dcea82dead76b69090b0bd4b178bd386e3023ceda333ff26126d0eada25329

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

• Get an API key and run your first validation
• Connect the MCP server to your coding assistant
• Inspect the ALPHAC engine on GitHub
• Read the MCP server source and integration examples

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000008670.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))