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Research Frontiers Incorporated: diluted weighted-average shares

Research Frontiers diluted weighted-average shares: 33.6 million shares for the year ending 2025-12-31, up 0.4% on the year. 4 values since 2022.

All Research Frontiers Incorporated financial histories

At a glance

Research Frontiers reported diluted weighted-average shares of 33.6 million shares for the year ending 2025-12-31, up 0.4% from 33.5 million shares a year earlier. From 2022 to 2025 it grew at a compound 1.6% a year, from 32.1 million shares to 33.6 million shares. Across the last four reported years it ranged from 32.1 million shares (2022-12-31) to 33.6 million shares (2025-12-31).

Computed from the values in the table below, in shares; each links to the filing it came from.

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 2026-09-19.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-3133,648,221shares2026-03-0510-K · 0001493152-26-008988 SEC
2024-01-012024-12-3133,520,904shares2026-03-0510-K · 0001493152-26-008988 SEC
2023-01-012023-12-3133,453,627shares2025-03-0610-K · 0001493152-25-009377 SEC
2022-01-012022-12-3132,070,233shares2024-03-0710-K · 0001493152-24-009207 SEC

Related RESEARCH FRONTIERS INCORPORATED histories

All 40 Research Frontiers histories

Inspect the source

Entity
RESEARCH FRONTIERS INCORPORATED / CIK 0000793524
Captured
SEC response SHA-256
515f42a584fc3f2d3e6a52e9bb6cda5cfb6d01a853f09030ae4f76d390b3492a

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.

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000793524.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"])))