COLUMBIA SPORTSWEAR COMPANY: 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 2009-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Related financial histories

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

Inspect the source

Entity

COLUMBIA SPORTSWEAR COMPANY / CIK 0001050797

Captured

2026-09-20T05:17:18.000Z

SEC response SHA-256

41c86dc8f1831bd5fb6fdaddb41d81ff3fa803ee288eb69b5dfb0678f8182610

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/0001050797.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"])))