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LITTELFUSE INC /DE: diluted weighted-average shares

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

All LITTELFUSE INC /DE 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 2008-12-28 to 2025-12-27. The SEC response was captured on 2026-09-20.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-12-292025-12-2724,817,000shares2026-02-1910-K · 0001628280-26-009585
2023-12-312024-12-2825,039,000shares2026-02-1910-K · 0001628280-26-009585
2023-01-012023-12-3025,102,000shares2026-02-1910-K · 0001628280-26-009585
2022-01-022022-12-3124,986,000shares2025-03-1310-K · 0000889331-25-000039
2020-12-272022-01-0124,932,000shares2024-02-1610-K · 0000889331-24-000034
2019-12-292020-12-2624,592,000shares2023-02-1610-K · 0000889331-23-000016
2018-12-302019-12-2824,818,000shares2022-02-1710-K · 0000889331-22-000011
2017-12-312018-12-2925,235,000shares2021-02-1810-K · 0000889331-21-000010
2017-01-012017-12-3022,931,000shares2020-02-2110-K · 0000889331-20-000014
2016-01-032016-12-3122,727,000shares2019-02-2210-K · 0000889331-19-000015
2015-01-042016-01-0222,719,000shares2016-03-0110-K · 0001437749-16-026441
2014-12-282016-01-0222,719,000shares2018-02-2310-K · 0001437749-18-003245
2013-12-292014-12-2722,727,000shares2017-02-2710-K · 0001437749-17-003344
2012-12-302013-12-2822,537,000shares2016-03-0110-K · 0001437749-16-026441
2012-01-012012-12-2922,098,000shares2015-02-2410-K · 0001437749-15-003300
2011-01-022011-12-3122,255,000shares2014-07-2310-K · 0001437749-14-013149
2010-01-032011-01-0122,214,000shares2013-02-2710-K · 0001437749-13-002025
2008-12-282010-01-0221,812,000shares2012-02-2410-K · 0001437749-12-001722

Related financial histories

Inspect the source

Entity
LITTELFUSE INC /DE / CIK 0000889331
Captured
2026-09-20T05:03:28.726Z
SEC response SHA-256
8c492c3adb7371b6e58125a958fe456779f3bf8ca48e514d5870abb4d227dc6a

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/0000889331.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"])))