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KODIAK SCIENCES INC.: diluted weighted-average shares

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

All KODIAK SCIENCES INC. financial histories

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 2020-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

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.

Context from the filing

For 2023–2025, Kodiak uses net loss attributable to common stockholders as its EPS numerator and excludes potential common shares as antidilutive in the loss periods. The 2023 comprehensive-loss figure is a separate measure and does not replace net loss for EPS. Shares and per-share amounts are exempt from the thousands heading and use scale-zero tags. Excluded common-share equivalents are not added to the weighted-average denominator. Read the source filing.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-3153,208,311shares2026-03-3110-K · 0001193125-26-134887
2024-01-012024-12-3152,583,148shares2026-03-3110-K · 0001193125-26-134887
2023-01-012023-12-3152,414,256shares2026-03-3110-K · 0001193125-26-134887
2022-01-012022-12-3152,249,620shares2025-03-2710-K · 0000950170-25-046098
2021-01-012021-12-3151,788,918shares2024-03-2810-K · 0000950170-24-038025
2020-01-012020-12-3145,741,845shares2023-03-2810-K · 0000950170-23-010314

Related financial histories

Inspect the source

Entity
KODIAK SCIENCES INC. / CIK 0001468748
Captured
2026-09-20T09:07:12.598Z
SEC response SHA-256
fa937f3241c45706b9bd1bff3d57d7c6bde0b88302772aa7c62bc4ffe51766d7

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/0001468748.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"])))