AI FINANCIAL CORPORATION: 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 2010-01-03 to 2025-12-27. The SEC response was captured on 2026-09-20.

Selected filing history

Related financial histories

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

Inspect the source

Entity

AI FINANCIAL CORPORATION / CIK 0000862861

Captured

2026-09-20T04:59:48.119Z

SEC response SHA-256

b5e952a29c52e609e4d4079dcc4d7c2058d4209c591c2d8251622c54f17e895f

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/0000862861.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"])))