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Oxford Industries Inc: contract revenue excluding tax

Oxford Industries contract revenue excluding tax: $1.48 billion for the year ending 2026-01-31, down 2.6% on the year. 11 values since 2016 from SEC filings.

All Oxford Industries Inc financial histories

At a glance

Oxford Industries reported contract revenue excluding tax of $1.48 billion for the year ending 2026-01-31, down 2.6% from $1.52 billion a year earlier. From 2016 to 2026 it grew at a compound 4.3% a year, from $969.3 million to $1.48 billion. Across the last five reported years it ranged from $1.14 billion (2022-01-29) to $1.57 billion (2024-02-03).

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Revenue from customer contracts excluding assessed taxes under this specific taxonomy concept. It is not automatically comparable to older revenue tags.

Exact concept: us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax. 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 2015-02-01 to 2026-01-31. The SEC response was captured on 2026-09-19.

Selected filing history

Contract revenue excluding tax in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-02-022026-01-311,477,834,000USD2026-03-2710-K · 0000075288-26-000026 SEC
2024-02-042025-02-011,516,601,000USD2026-03-2710-K · 0000075288-26-000026 SEC
2023-01-292024-02-031,571,475,000USD2026-03-2710-K · 0000075288-26-000026 SEC
2022-01-302023-01-281,411,528,000USD2025-03-3110-K · 0000075288-25-000033 SEC
2021-01-312022-01-291,142,079,000USD2024-04-0110-K · 0000075288-24-000003 SEC
2020-02-022021-01-30748,833,000USD2023-03-2810-K · 0000075288-23-000005 SEC
2019-02-032020-02-011,122,790,000USD2022-03-2810-K · 0000075288-22-000004 SEC
2018-02-042019-02-021,107,466,000USD2021-03-2910-K · 0000075288-21-000004 SEC
2017-01-292018-02-031,086,211,000USD2020-03-3010-K · 0001558370-20-003379 SEC
2016-01-312017-01-281,022,588,000USD2019-04-0110-K · 0000075288-19-000024 SEC
2015-02-012016-01-30969,290,000USD2018-04-0210-K · 0000075288-18-000018 SEC

Related OXFORD INDUSTRIES INC histories

All 59 Oxford Industries histories

Inspect the source

Entity
OXFORD INDUSTRIES INC / CIK 0000075288
Captured
SEC response SHA-256
4324bea54fdefa1df1be62f59861b80d70e6b61c76d902e8c4e4c324c697a690

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.

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000075288.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"])))