Siebert Financial Corp.: diluted weighted-average shares
Siebert Financial diluted weighted-average shares: 37.1 million shares for the year ending 2023-12-31, up 14.4% on the year. 3 values since 2021.
All Siebert Financial Corp. financial histories
At a glance
Siebert Financial reported diluted weighted-average shares of 37.1 million shares for the year ending 2023-12-31, up 14.4% from 32.4 million shares a year earlier. Across the last three reported years it ranged from 31.3 million shares (2021-12-31) to 37.1 million shares (2023-12-31).
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 2021-01-01 to 2023-12-31. The SEC response was captured on 2026-09-19.
This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.
Withheld reporting periods
us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding: This diluted weighted-average share observation is withheld because the main statement combines basic and diluted denominators, while Note 19 reports a higher diluted total after adding unvested-share dilution for the same year. Equal rounded earnings per share do not establish equal denominators. The conflicting source presentations remain retained; no replacement value or tag correction has been applied.
Inspect 2 withheld observations (period ends: 2024-12-31 to 2025-12-31)
| Period start | Period end | Original value | Unit | Source filing |
|---|---|---|---|---|
| 2024-01-01 | 2024-12-31 | 39951510 | shares | 0001213900-26-036500 |
| 2025-01-01 | 2025-12-31 | 40362780 | shares | 0001213900-26-036500 |
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| 2023-01-01 | 2023-12-31 | 37,070,366 | shares | 2025-04-01 | 10-K/A · 0001213900-25-026597 SEC |
| 2022-01-01 | 2022-12-31 | 32,408,449 | shares | 2024-05-10 | 10-K · 0001213900-24-041746 SEC |
| 2021-01-01 | 2021-12-31 | 31,316,119 | shares | 2023-03-29 | 10-K · 0001213900-23-024165 SEC |
Related Siebert Financial Corp. histories
Inspect the source
- Entity
- Siebert Financial Corp. / CIK 0000065596
- Captured
- SEC response SHA-256
daeca46d576d2ff5c72e9a029274ce4aed4a696365902d8ba9560911d4b7ee50
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.
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Read the published dataset with Python
import json
from urllib.request import urlopen
with urlopen("https://canlicapital.com/company-data/0000065596.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"])))