FS Credit Real Estate Income Trust, Inc.: diluted weighted-average shares

What this measure means

Weighted-average shares used for diluted earnings per share. Potential shares are included under the applicable dilution rules, not simply added to outstanding shares.

Exact concept: us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding. 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 2020-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.

Selected filing history

Related financial histories

• FS Credit Real Estate Income Trust, Inc.: total assets
• FS Credit Real Estate Income Trust, Inc.: total liabilities
• FS Credit Real Estate Income Trust, Inc.: stockholders equity
• FS Credit Real Estate Income Trust, Inc.: cash and cash equivalents
• FS Credit Real Estate Income Trust, Inc.: net income or loss
• FS Credit Real Estate Income Trust, Inc.: operating cash flow
• FS Credit Real Estate Income Trust, Inc.: financing cash flow
• FS Credit Real Estate Income Trust, Inc.: investing cash flow
• FS Credit Real Estate Income Trust, Inc.: retained earnings or deficit
• FS Credit Real Estate Income Trust, Inc.: basic weighted-average shares
• FS Credit Real Estate Income Trust, Inc.: basic earnings per share
• FS Credit Real Estate Income Trust, Inc.: diluted earnings per share
• FS Credit Real Estate Income Trust, Inc.: income tax expense or benefit
• FS Credit Real Estate Income Trust, Inc.: interest expense
• FS Credit Real Estate Income Trust, Inc.: net finite-lived intangible assets
• FS Credit Real Estate Income Trust, Inc.: common-stock repurchase payments

Inspect the source

Entity

FS Credit Real Estate Income Trust, Inc. / CIK 0001690536

Captured

2026-09-21T17:24:06.660Z

SEC response SHA-256

a9340ccd4520163e6215bb325be3de2dce688b83d434022719a4961d8e4df721

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/0001690536.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"])))