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SYNAPTICS INCORPORATED: gross profit

Gross profit for SYNAPTICS INCORPORATED. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All SYNAPTICS INCORPORATED financial histories

What this measure means

Revenue less the costs directly attributed to the goods or services sold. It precedes other operating expenses and is not net income.

Exact concept: us-gaap:GrossProfit. 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 2008-06-29 to 2026-06-27. The SEC response was captured on 2026-09-19.

Selected filing history

Gross profit in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-06-292026-06-27535,400,000USD2026-08-1010-K · 0000817720-26-000055
2024-06-302025-06-28480,400,000USD2026-08-1010-K · 0000817720-26-000055
2023-06-252024-06-29439,800,000USD2026-08-1010-K · 0000817720-26-000055
2022-06-262023-06-24715,900,000USD2025-08-2110-K · 0000817720-25-000073
2021-06-272022-06-25943,100,000USD2024-08-2310-K · 0000950170-24-100261
2020-06-282021-06-26611,200,000USD2023-08-1810-K · 0000950170-23-043521
2019-06-302020-06-27543,100,000USD2022-08-2210-K · 0000950170-22-017628
2018-07-012019-06-29497,100,000USD2021-08-2310-K · 0001564590-21-045326
2017-06-252018-06-30480,100,000USD2020-08-2110-K · 0001564590-20-040946
2016-06-262017-06-24523,600,000USD2019-08-2310-K · 0001564590-19-032908
2015-06-282016-06-25581,500,000USD2018-08-2410-K · 0001564590-18-022311
2014-06-292015-06-27578,700,000USD2017-08-1810-K · 0001564590-17-017839
2013-06-302014-06-28436,100,000USD2016-08-2610-K · 0001564590-16-024739
2012-07-012013-06-29325,800,000USD2015-08-2510-K · 0001564590-15-007555
2011-06-262012-06-30255,567,000USD2014-08-2210-K · 0001193125-14-318722
2010-06-272011-06-25246,070,000USD2013-08-1910-K · 0001193125-13-339377
2009-07-012010-06-26208,702,000USD2012-09-2110-K/A · 0001193125-12-399914
2009-06-282010-06-26208,702,000USD2011-08-2210-K · 0000950123-11-079162
2008-06-292009-06-27191,509,000USD2011-08-2210-K · 0000950123-11-079162

Related financial histories

Inspect the source

Entity
SYNAPTICS INCORPORATED / CIK 0000817720
Captured
2026-09-19T15:07:00.568Z
SEC response SHA-256
5664a9aa1ff673d66ff5373f204c8531a51738596073393aa7ab8ebc445809b3

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/0000817720.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"])))