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Roblox Corporation: diluted earnings per share

Diluted earnings per share for Roblox Corporation. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Roblox Corporation financial histories

What this measure means

Reported earnings or loss per share under dilution rules. Antidilutive instruments may be excluded. A diluted value can equal the basic value without implying no potential dilution.

Exact concept: us-gaap:EarningsPerShareDiluted. 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 2019-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Reading these values

This selected numerical history matches Basic earnings per share 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, Roblox uses loss attributable to common stockholders after noncontrolling interests as its EPS numerator, rather than consolidated net loss. Potential common-stock equivalents are excluded as anti-dilutive for all periods presented. Weighted-average share counts are reported in thousands and tagged with scale three; EPS uses scale zero. The equal basic and diluted denominators are alternative measures and should not be added together. Read the source filing.

Selected filing history

Diluted earnings per share in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31-1.54USD/shares2026-02-1110-K · 0001315098-26-000024
2024-01-012024-12-31-1.44USD/shares2026-02-1110-K · 0001315098-26-000024
2023-01-012023-12-31-1.87USD/shares2026-02-1110-K · 0001315098-26-000024
2022-01-012022-12-31-1.55USD/shares2025-02-1810-K · 0001315098-25-000033
2021-01-012021-12-31-0.97USD/shares2024-02-2110-K · 0001315098-24-000026
2020-01-012020-12-31-1.39USD/shares2023-02-2810-K · 0001315098-23-000035
2019-01-012019-12-31-0.44USD/shares2022-02-2510-K · 0001315098-22-000058

Related financial histories

Inspect the source

Entity
Roblox Corporation / CIK 0001315098
Captured
2026-09-20T07:51:39.363Z
SEC response SHA-256
da93dfbe41ad0be3c80f12e25819e45a48e960678647dc8d7235efdfce5aa481

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/0001315098.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"])))