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AUDIOEYE INC: basic weighted-average shares

Basic weighted-average shares for AUDIOEYE INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All AUDIOEYE INC financial histories

What this measure means

Time-weighted shares used for basic earnings per share. This denominator differs from shares outstanding at a single reporting date.

Exact concept: us-gaap:WeightedAverageNumberOfSharesOutstandingBasic. 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 2026-09-20.

Reading these values

This selected numerical history matches Diluted 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.

Context from the filing

AudioEye's 2024 and 2025 statements display dollar losses and weighted-average shares in thousands, except for per-share amounts. The share tags therefore represent 11,888,000 and 12,416,000 shares, respectively. Options and restricted stock units are potential common shares under the treasury stock method; during losses, common stock equivalents are excluded as anti-dilutive, so basic and diluted loss EPS are equal. Read the source filing.

Selected filing history

Basic weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-3112,416,000shares2026-03-1210-K · 0001104659-26-027159
2024-01-012024-12-3111,888,000shares2026-03-1210-K · 0001104659-26-027159
2023-01-012023-12-3111,766,000shares2025-03-1210-K · 0001558370-25-002819
2022-01-012022-12-3111,477,000shares2024-03-0710-K · 0001410578-24-000150
2021-01-012021-12-3111,040,000shares2023-03-0910-K · 0001410578-23-000236
2020-01-012020-12-319,313,000shares2022-03-1110-K · 0001410578-22-000353

Related financial histories

Inspect the source

Entity
AUDIOEYE INC / CIK 0001362190
Captured
2026-09-20T07:56:15.036Z
SEC response SHA-256
ff98d56fcf789f6a2b9b81e6ac2cc37d42092c28bddca9bce294d53b1d2e77bc

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.

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

with urlopen("https://canlicapital.com/company-data/0001362190.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"])))