MultiSensor AI Holdings, 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 2022-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.

Reading these values

This selected numerical history matches Basic weighted-average shares 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

• MultiSensor AI Holdings, Inc.: total assets
• MultiSensor AI Holdings, Inc.: total liabilities
• MultiSensor AI Holdings, Inc.: stockholders equity
• MultiSensor AI Holdings, Inc.: cash and cash equivalents
• MultiSensor AI Holdings, Inc.: net income or loss
• MultiSensor AI Holdings, Inc.: operating cash flow
• MultiSensor AI Holdings, Inc.: contract revenue excluding tax
• MultiSensor AI Holdings, Inc.: financing cash flow
• MultiSensor AI Holdings, Inc.: investing cash flow
• MultiSensor AI Holdings, Inc.: retained earnings or deficit
• MultiSensor AI Holdings, Inc.: basic weighted-average shares
• MultiSensor AI Holdings, Inc.: basic earnings per share
• MultiSensor AI Holdings, Inc.: diluted earnings per share
• MultiSensor AI Holdings, Inc.: income tax expense or benefit
• MultiSensor AI Holdings, Inc.: net property, plant and equipment
• MultiSensor AI Holdings, Inc.: share-based compensation expense
• MultiSensor AI Holdings, Inc.: operating income or loss
• MultiSensor AI Holdings, Inc.: current assets
• MultiSensor AI Holdings, Inc.: current liabilities
• MultiSensor AI Holdings, Inc.: current accounts payable
• MultiSensor AI Holdings, Inc.: net current accounts receivable
• MultiSensor AI Holdings, Inc.: operating expenses
• MultiSensor AI Holdings, Inc.: net inventory
• MultiSensor AI Holdings, Inc.: selling, general and administrative expense

Inspect the source

Entity

MultiSensor AI Holdings, Inc. / CIK 0001863990

Captured

2026-09-21T17:33:36.402Z

SEC response SHA-256

3d6bfc1677f8b6e3b6cdf567fa50cfdd8a6a9f23a526868ee15e830e7fcaaf85

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/0001863990.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"])))