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GIBRALTAR INDUSTRIES, INC.: basic earnings per share

Basic earnings per share for GIBRALTAR INDUSTRIES, INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All GIBRALTAR INDUSTRIES, INC. financial histories

What this measure means

Reported earnings or loss per basic common share or unit. Inspect attribution, share classes and restatements before comparing periods. This is not a market return.

Exact concept: us-gaap:EarningsPerShareBasic. 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 2008-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Basic earnings per share in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31-1.48USD/shares2026-02-2610-K · 0000912562-26-000025
2024-01-012024-12-314.5USD/shares2026-02-2610-K · 0000912562-26-000025
2023-01-012023-12-313.61USD/shares2026-02-2610-K · 0000912562-26-000025
2022-01-012022-12-312.57USD/shares2025-02-1910-K · 0000912562-25-000007
2021-01-012021-12-312.3USD/shares2024-02-2110-K · 0000912562-24-000012
2020-01-012020-12-311.98USD/shares2023-02-2210-K · 0000912562-23-000009
2019-01-012019-12-312.01USD/shares2022-02-2310-K · 0000912562-22-000009
2018-01-012018-12-312USD/shares2021-02-2510-K · 0000912562-21-000007
2017-01-012017-12-311.97USD/shares2020-02-2810-K · 0000912562-20-000007
2016-01-012016-12-311.07USD/shares2019-02-2710-K · 0000912562-19-000009
2015-01-012015-12-310.75USD/shares2018-02-2710-K · 0000912562-18-000006
2014-01-012014-12-31-2.63USD/shares2017-02-2110-K · 0000912562-17-000008
2013-01-012013-12-31-0.18USD/shares2016-02-1810-K · 0000912562-16-000073
2012-01-012012-12-310.41USD/shares2015-02-2410-K · 0000912562-15-000009
2011-01-012011-12-310.54USD/shares2014-02-2010-K · 0001193125-14-060904
2010-01-012010-12-31-3.01USD/shares2013-02-2210-K · 0001193125-13-071754
2009-01-012009-12-31-1.73USD/shares2012-02-2410-K · 0001193125-12-078110
2008-01-012008-12-310.8USD/shares2011-02-2510-K · 0000950123-11-018507

Related financial histories

Inspect the source

Entity
GIBRALTAR INDUSTRIES, INC. / CIK 0000912562
Captured
2026-09-20T05:06:30.444Z
SEC response SHA-256
45fbabdea6ea8eb791e2c81c13e244a8cbd8962a823acc4610d6a74ecf021598

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/0000912562.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"])))