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PRESTIGE CONSUMER HEALTHCARE INC.: diluted earnings per share

Diluted earnings per share for PRESTIGE CONSUMER HEALTHCARE INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All PRESTIGE CONSUMER HEALTHCARE INC. 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 2009-04-01 to 2026-03-31. The SEC response was captured on 2026-09-20.

Selected filing history

Diluted earnings per share in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-04-012026-03-313.91USD/shares2026-05-1410-K · 0001295947-26-000016
2024-04-012025-03-314.29USD/shares2026-05-1410-K · 0001295947-26-000016
2023-04-012024-03-314.17USD/shares2026-05-1410-K · 0001295947-26-000016
2022-04-012023-03-31-1.65USD/shares2025-05-0910-K · 0001295947-25-000017
2021-04-012022-03-314.04USD/shares2024-05-1510-K · 0001295947-24-000017
2020-04-012021-03-313.25USD/shares2023-05-0510-K · 0001295947-23-000017
2019-04-012020-03-312.78USD/shares2022-05-0610-K · 0001295947-22-000015
2018-04-012019-03-31-0.69USD/shares2021-05-0710-K · 0001295947-21-000021
2017-04-012018-03-316.34USD/shares2020-05-0810-K · 0001295947-20-000018
2016-04-012017-03-311.3USD/shares2019-05-1310-K · 0001295947-19-000015
2015-04-012016-03-311.88USD/shares2018-05-1010-K · 0001295947-18-000013
2014-04-012015-03-311.49USD/shares2017-05-1710-K · 0001295947-17-000018
2013-04-012014-03-311.39USD/shares2016-05-1710-K · 0001295947-16-000051
2012-04-012013-03-311.27USD/shares2015-05-1410-K · 0001295947-15-000015
2011-04-012012-03-310.73USD/shares2014-05-1910-K · 0001295947-14-000015
2010-04-012011-03-310.58USD/shares2013-05-1710-K · 0001295947-13-000018
2009-04-012010-03-310.64USD/shares2012-05-1810-K · 0001295947-12-000029

Related financial histories

Inspect the source

Entity
PRESTIGE CONSUMER HEALTHCARE INC. / CIK 0001295947
Captured
2026-09-20T07:50:20.947Z
SEC response SHA-256
7743f3e37e670fd69592b0c4961b7707545f13f7bed382d3a14d3019f6d3b4c6

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/0001295947.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"])))