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Dolby Laboratories, Inc.: operating income or loss

Operating income or loss for Dolby Laboratories, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Dolby Laboratories, Inc. financial histories

What this measure means

Operating revenue less operating expenses for the reporting period. It excludes items outside the reported operating result and is not free cash flow.

Exact concept: us-gaap:OperatingIncomeLoss. 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 2007-09-29 to 2025-09-26. The SEC response was captured on 2026-09-20.

Selected filing history

Operating income or loss in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-09-282025-09-26264,959,000USD2025-11-1810-K · 0001308547-25-000007
2023-09-302024-09-27258,326,000USD2025-11-1810-K · 0001308547-25-000007
2022-10-012023-09-29215,753,000USD2025-11-1810-K · 0001308547-25-000007
2021-09-252022-09-30206,605,000USD2024-11-1910-K · 0001628280-24-048519
2020-09-262021-09-24344,390,000USD2023-11-1710-K · 0001628280-23-039389
2019-09-282020-09-25218,742,000USD2022-11-1810-K · 0001628280-22-030390
2018-09-292019-09-27257,077,000USD2021-11-1710-K · 0001628280-21-023630
2017-09-302018-09-28183,505,000USD2020-11-1610-K · 0001628280-20-016459
2016-10-012017-09-29247,133,000USD2019-11-2510-K · 0001628280-19-014472
2015-09-262016-09-30231,795,000USD2018-11-1510-K · 0001628280-18-014390
2014-09-272015-09-25213,228,000USD2017-11-1610-K · 0001628280-17-011633
2013-09-282014-09-26273,718,000USD2016-11-1810-K · 0001628280-16-021559
2012-09-292013-09-27245,262,000USD2015-11-2410-K · 0001628280-15-008974
2011-10-012012-09-28361,992,000USD2014-11-1810-K · 0001445305-14-005158
2010-09-252011-09-30429,733,000USD2013-11-1510-K · 0001308547-13-000011
2009-09-262010-09-24429,381,000USD2012-11-1510-K · 0001308547-12-000010
2008-09-272009-09-25363,666,000USD2011-11-2310-K · 0001193125-11-320293
2007-09-292008-09-26286,783,000USD2010-11-2210-K · 0001193125-10-266157

Related financial histories

Inspect the source

Entity
Dolby Laboratories, Inc. / CIK 0001308547
Captured
2026-09-20T07:51:10.545Z
SEC response SHA-256
cc65a6f34013f443da51e389f537bca7ca8bca8cc3e2da20106c70e8530cc05b

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/0001308547.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"])))