GENUINE PARTS CO: diluted earnings per share

What this measure means

Reported earnings or loss per share under dilution rules. Antidilutive instruments may be excluded. A diluted value can equal the basic value without implying no potential dilution.

Exact concept: us-gaap:EarningsPerShareDiluted. 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 2007-01-01 to 2025-12-31. The SEC response was captured on 2026-09-19.

Selected filing history

Related financial histories

• GENUINE PARTS CO: total assets
• GENUINE PARTS CO: stockholders equity
• GENUINE PARTS CO: cash and cash equivalents
• GENUINE PARTS CO: net income or loss
• GENUINE PARTS CO: operating cash flow
• GENUINE PARTS CO: capital expenditure payments
• GENUINE PARTS CO: contract revenue excluding tax
• GENUINE PARTS CO: financing cash flow
• GENUINE PARTS CO: investing cash flow
• GENUINE PARTS CO: retained earnings or deficit
• GENUINE PARTS CO: basic weighted-average shares
• GENUINE PARTS CO: diluted weighted-average shares
• GENUINE PARTS CO: basic earnings per share
• GENUINE PARTS CO: income tax expense or benefit
• GENUINE PARTS CO: net property, plant and equipment
• GENUINE PARTS CO: share-based compensation expense
• GENUINE PARTS CO: operating income or loss
• GENUINE PARTS CO: current assets
• GENUINE PARTS CO: interest expense
• GENUINE PARTS CO: current liabilities
• GENUINE PARTS CO: current accounts payable
• GENUINE PARTS CO: goodwill carrying amount
• GENUINE PARTS CO: net finite-lived intangible assets
• GENUINE PARTS CO: common-stock repurchase payments
• GENUINE PARTS CO: operating expenses
• GENUINE PARTS CO: net inventory
• GENUINE PARTS CO: gross profit
• GENUINE PARTS CO: selling, general and administrative expense

Inspect the source

Entity

GENUINE PARTS CO / CIK 0000040987

Captured

2026-09-19T14:47:25.223Z

SEC response SHA-256

d683bb80d340a8e8e77d2b1069a5df9fb984ba0a77ccf31d3124d4952e597c8a

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/0000040987.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"])))