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KELLY SERVICES, INC.: diluted weighted-average shares

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

All KELLY SERVICES, 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 2008-12-29 to 2025-12-28. The SEC response was captured on 2026-09-19.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-12-302025-12-2835,100,000shares2026-02-1210-K · 0000055135-26-000053
2024-01-012024-12-2935,500,000shares2026-02-1210-K · 0000055135-26-000053
2023-01-022023-12-3136,300,000shares2026-02-1210-K · 0000055135-26-000053
2022-01-032023-01-0138,100,000shares2025-02-1310-K · 0000055135-25-000007
2021-01-042022-01-0239,500,000shares2024-02-2010-K · 0000055135-24-000007
2019-12-302021-01-0339,300,000shares2023-02-1610-K · 0000055135-23-000005
2018-12-312019-12-2939,200,000shares2022-02-1710-K · 0000055135-22-000006
2018-01-012018-12-3039,100,000shares2021-02-1810-K · 0000055135-21-000004
2017-01-022017-12-3139,000,000shares2020-02-1310-K · 0000055135-20-000006
2016-01-042017-01-0138,400,000shares2019-02-1410-K · 0000055135-19-000006
2014-12-292016-01-0337,900,000shares2018-02-2010-K · 0000055135-18-000006
2013-12-302014-12-2837,500,000shares2017-02-1710-K · 0000055135-17-000008
2012-12-312013-12-2937,300,000shares2016-02-1810-K · 0000055135-16-000093
2012-01-022012-12-3037,000,000shares2015-02-1210-K · 0000055135-15-000009
2011-01-032012-01-0136,800,000shares2014-02-1310-K · 0001437749-14-002057
2010-01-042011-01-0236,100,000shares2013-02-1410-K · 0001437749-13-001540
2008-12-292010-01-0334,900,000shares2012-02-1610-K · 0000950123-12-002654

Related financial histories

Inspect the source

Entity
KELLY SERVICES, INC. / CIK 0000055135
Captured
2026-09-19T14:48:25.668Z
SEC response SHA-256
8b40b85cf8ec33b3e0ea8fae68f843b38e6ab6e5481a61ecf4bd4cd4411bad4e

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/0000055135.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"])))