FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: operating cash flow

What this measure means

Cash generated or used by operating activities. Working-capital timing can make this differ substantially from reported income.

Exact concept: us-gaap:NetCashProvidedByUsedInOperatingActivities. 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

• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: total assets
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: total liabilities
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: stockholders equity
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: cash and cash equivalents
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: net income or loss
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: financing cash flow
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: investing cash flow
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: retained earnings or deficit
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: basic weighted-average shares
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: diluted weighted-average shares
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: basic earnings per share
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: diluted earnings per share
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: income tax expense or benefit
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: interest expense
• FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE: selling, general and administrative expense

Inspect the source

Entity

FEDERAL NATIONAL MORTGAGE ASSOCIATION FANNIE MAE / CIK 0000310522

Captured

2026-09-19T14:55:46.176Z

SEC response SHA-256

58952e767238c49d191d763533d392b23599f3516bc866eb1c4e084b1e1a2f18

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/0000310522.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"])))