GENWORTH FINANCIAL, INC.: 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 2026-09-20.

Selected filing history

Related financial histories

• GENWORTH FINANCIAL, INC.: total assets
• GENWORTH FINANCIAL, INC.: total liabilities
• GENWORTH FINANCIAL, INC.: stockholders equity
• GENWORTH FINANCIAL, INC.: cash and cash equivalents
• GENWORTH FINANCIAL, INC.: net income or loss
• GENWORTH FINANCIAL, INC.: operating cash flow
• GENWORTH FINANCIAL, INC.: revenue
• GENWORTH FINANCIAL, INC.: financing cash flow
• GENWORTH FINANCIAL, INC.: investing cash flow
• GENWORTH FINANCIAL, INC.: retained earnings or deficit
• GENWORTH FINANCIAL, INC.: basic weighted-average shares
• GENWORTH FINANCIAL, INC.: diluted weighted-average shares
• GENWORTH FINANCIAL, INC.: basic earnings per share
• GENWORTH FINANCIAL, INC.: income tax expense or benefit
• GENWORTH FINANCIAL, INC.: share-based compensation expense
• GENWORTH FINANCIAL, INC.: operating income or loss
• GENWORTH FINANCIAL, INC.: interest expense
• GENWORTH FINANCIAL, INC.: goodwill carrying amount
• GENWORTH FINANCIAL, INC.: common-stock repurchase payments
• GENWORTH FINANCIAL, INC.: selling, general and administrative expense

Inspect the source

Entity

GENWORTH FINANCIAL, INC. / CIK 0001276520

Captured

2026-09-20T07:48:13.239Z

SEC response SHA-256

2b6d25fa03535c8ffe9f28a6f0105b191e206a908e8de274b94f8542fd4008fb

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/0001276520.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"])))