ALLEGHENY ENERGY, INC: diluted weighted-average shares

What this measure means

Weighted-average shares used for diluted earnings per share. Potential shares are included under the applicable dilution rules, not simply added to outstanding shares.

Exact concept: us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding. 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 2010-12-31. The SEC response was captured on 2026-09-23.

This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.

Selected filing history

Related ALLEGHENY ENERGY, INC histories

• Total assets
• Stockholders equity
• Cash and cash equivalents
• Net income or loss
• Operating cash flow
• Capital expenditure payments
• Revenue
• Financing cash flow
• Investing cash flow
• Retained earnings or deficit
• Basic weighted-average shares
• Basic earnings per share
• Diluted earnings per share
• Income tax expense or benefit
• Net property, plant and equipment
• Operating income or loss
• Current assets
• Interest expense
• Current liabilities
• Current accounts payable
• Net inventory
• Accumulated other comprehensive income or loss
• Total equity including noncontrolling interests
• Accumulated depreciation on property, plant and equipment
• Other noncurrent assets
• Other noncurrent liabilities
• Profit or loss including noncontrolling interests
• Other nonoperating income or expense
• Interest paid, net
• Income taxes paid, net
• Change in accounts receivable
• Change in inventories
• Change in accounts payable

Inspect the source

Entity

ALLEGHENY ENERGY, INC / CIK 0000003673

Captured

2026-09-23 02:38 UTC

SEC response SHA-256

9a0c54db759f386e8db7d52061c093ea98a8a7e3f7e536c91b0eedb40ae05a00

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/0000003673.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"])))