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Great Lakes Dredge & Dock Corporation: diluted earnings per share

Diluted earnings per share for Great Lakes Dredge & Dock Corporation. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Great Lakes Dredge & Dock 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 2009-01-01 to 2025-12-31. The SEC response was captured on 2026-09-21.

Selected filing history

Diluted earnings per share in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-311.08USD/shares2026-03-0910-K/A · 0001193125-26-097043
2024-01-012024-12-310.84USD/shares2026-03-0910-K/A · 0001193125-26-097043
2023-01-012023-12-310.21USD/shares2026-03-0910-K/A · 0001193125-26-097043
2022-01-012022-12-31-0.52USD/shares2025-02-2110-K · 0000950170-25-024407
2021-01-012021-12-310.75USD/shares2024-02-1610-K · 0000950170-24-016412
2020-01-012020-12-311USD/shares2023-02-1710-K · 0000950170-23-003211
2019-01-012019-12-310.76USD/shares2022-02-2310-K · 0001564590-22-006231
2018-01-012018-12-31-0.1USD/shares2021-02-2410-K · 0001564590-21-008165
2017-01-012017-12-31-0.51USD/shares2020-02-2610-K · 0001564590-20-006778
2016-01-012016-12-31-0.13USD/shares2019-02-2610-K · 0001564590-19-004521
2015-01-012015-12-31-0.1USD/shares2018-03-0110-K · 0001564590-18-003940
2014-01-012014-12-310.17USD/shares2017-02-2810-K · 0001564590-17-002889
2013-01-012013-12-31-0.57USD/shares2016-02-2910-K · 0001564590-16-013760
2012-01-012012-12-31-0.04USD/shares2015-03-0610-K · 0001193125-15-081253
2011-01-012011-12-310.28USD/shares2014-03-1110-K · 0001193125-14-094004
2010-01-012010-12-310.59USD/shares2013-03-2910-K · 0001193125-13-134675
2009-01-012009-12-310.3USD/shares2012-03-0910-K · 0001193125-12-107139

Related financial histories

Inspect the source

Entity
Great Lakes Dredge & Dock Corporation / CIK 0001372020
Captured
2026-09-21T17:19:27.004Z
SEC response SHA-256
a9b0868fd4d9de5953211d7938513a4aa791445a01f88b297a4f92020f1ff16b

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/0001372020.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"])))