AMERICAN EXPRESS CO: net income or loss

What this measure means

Reported profit or loss for the period. Check the filing for attribution, exceptional items and discontinued operations before comparing companies.

Exact concept: us-gaap:NetIncomeLoss. 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

• AMERICAN EXPRESS CO: total assets
• AMERICAN EXPRESS CO: total liabilities
• AMERICAN EXPRESS CO: stockholders equity
• AMERICAN EXPRESS CO: cash and cash equivalents
• AMERICAN EXPRESS CO: operating cash flow
• AMERICAN EXPRESS CO: capital expenditure payments
• AMERICAN EXPRESS CO: revenue
• AMERICAN EXPRESS CO: contract revenue excluding tax
• AMERICAN EXPRESS CO: financing cash flow
• AMERICAN EXPRESS CO: investing cash flow
• AMERICAN EXPRESS CO: retained earnings or deficit
• AMERICAN EXPRESS CO: basic weighted-average shares
• AMERICAN EXPRESS CO: diluted weighted-average shares
• AMERICAN EXPRESS CO: basic earnings per share
• AMERICAN EXPRESS CO: diluted earnings per share
• AMERICAN EXPRESS CO: income tax expense or benefit
• AMERICAN EXPRESS CO: net property, plant and equipment
• AMERICAN EXPRESS CO: share-based compensation expense
• AMERICAN EXPRESS CO: operating income or loss
• AMERICAN EXPRESS CO: interest expense
• AMERICAN EXPRESS CO: goodwill carrying amount
• AMERICAN EXPRESS CO: net finite-lived intangible assets
• AMERICAN EXPRESS CO: common-stock repurchase payments

Inspect the source

Entity

AMERICAN EXPRESS CO / CIK 0000004962

Captured

2026-09-19T14:43:02.825Z

SEC response SHA-256

8fe514defe349eb117b04523e2e5371387cef70748eabd0445040fe0a9f4840f

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/0000004962.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"])))