Copper Property CTL Pass Through Trust: 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 2021-01-30 to 2025-12-31. The SEC response was captured on 2026-09-21.

Reading these values

This selected numerical history matches Basic earnings per share for the same reporting intervals and original units. The accounting definitions remain distinct. Equal values do not establish that the concepts are interchangeable or explain why they match; filing dates and accessions may differ. Compare the definitions and source filings before combining them.

Selected filing history

Related financial histories

• Copper Property CTL Pass Through Trust: total assets
• Copper Property CTL Pass Through Trust: total liabilities
• Copper Property CTL Pass Through Trust: stockholders equity
• Copper Property CTL Pass Through Trust: cash and cash equivalents
• Copper Property CTL Pass Through Trust: net income or loss
• Copper Property CTL Pass Through Trust: operating cash flow
• Copper Property CTL Pass Through Trust: financing cash flow
• Copper Property CTL Pass Through Trust: investing cash flow
• Copper Property CTL Pass Through Trust: retained earnings or deficit
• Copper Property CTL Pass Through Trust: basic earnings per share
• Copper Property CTL Pass Through Trust: net finite-lived intangible assets

Inspect the source

Entity

Copper Property CTL Pass Through Trust / CIK 0001837671

Captured

2026-09-21T17:20:43.175Z

SEC response SHA-256

a8e10fa8f79213873544d236dc8fa076d113e2ae7479209d6bd7b1a667ea972d

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/0001837671.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"])))