Digerati Technologies, Inc.: basic weighted-average shares
Digerati Technologies basic weighted-average shares: 151.5 million shares for the year ending 2023-07-31, up 8.5% on the year. 8 values since 2016.
All Digerati Technologies, Inc. financial histories
At a glance
Digerati Technologies reported basic weighted-average shares of 151.5 million shares for the year ending 2023-07-31, up 8.5% from 139.6 million shares a year earlier. From 2016 to 2023 it grew at a compound 62.0% a year, from 5.18 million shares to 151.5 million shares. Across the last five reported years it ranged from 16.7 million shares (2019-07-31) to 151.5 million shares (2023-07-31).
Computed from the values in the table below, in shares; each links to the filing it came from.
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 2015-08-01 to 2023-07-31. The SEC response was captured on 2026-09-20.
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.
Selected filing history
| Period start | Period end | Value | Unit | Filed | Source filing |
|---|---|---|---|---|---|
| 2022-08-01 | 2023-07-31 | 151,491,158 | shares | 2023-11-24 | 10-K · 0001213900-23-089905 SEC |
| 2021-08-01 | 2022-07-31 | 139,594,358 | shares | 2023-11-24 | 10-K · 0001213900-23-089905 SEC |
| 2020-08-01 | 2021-07-31 | 129,411,947 | shares | 2022-10-31 | 10-K · 0001213900-22-067679 SEC |
| 2019-08-01 | 2020-07-31 | 53,883,966 | shares | 2021-10-26 | 10-K · 0001213900-21-054631 SEC |
| 2018-08-01 | 2019-07-31 | 16,650,507 | shares | 2020-10-29 | 10-K · 0001213900-20-033960 SEC |
| 2017-08-01 | 2018-07-31 | 10,339,371 | shares | 2019-10-28 | 10-K · 0001213900-19-021294 SEC |
| 2016-08-01 | 2017-07-31 | 6,339,906 | shares | 2019-02-13 | 10-K · 0001213900-19-002388 SEC |
| 2015-08-01 | 2016-07-31 | 5,183,817 | shares | 2019-02-13 | 10-K · 0001213900-19-002354 SEC |
Related Digerati Technologies, Inc. histories
Inspect the source
- Entity
- Digerati Technologies, Inc. / CIK 0001014052
- Captured
- SEC response SHA-256
b100170c34b909aee6d8d594533d295e3a1851119a15c456fee01e7ad9067477
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
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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/0001014052.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"])))