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Ross Stores, Inc.: operating income or loss

Ross Stores operating income or loss: $2.71 billion for the year ending 2026-01-31, up 4.7% on the year. 4 values since 2023, each linked to its SEC filing.

All Ross Stores, Inc. financial histories

At a glance

Ross Stores reported operating income or loss of $2.71 billion for the year ending 2026-01-31, up 4.7% from $2.59 billion a year earlier. From 2023 to 2026 it grew at a compound 10.8% a year, from $1.99 billion to $2.71 billion. Across the last four reported years it ranged from $1.99 billion (2023-01-28) to $2.71 billion (2026-01-31). That was 11.9% of revenue ($22.8 billion) for the same year.

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

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 2022-01-30 to 2026-01-31. The SEC response was captured on 2026-09-19.

Selected filing history

Operating income or loss in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-02-022026-01-312,707,357,000USD2026-03-3110-K · 0000745732-26-000006 SEC
2024-02-042025-02-012,585,586,000USD2026-03-3110-K · 0000745732-26-000006 SEC
2023-01-292024-02-032,307,663,000USD2026-03-3110-K · 0000745732-26-000006 SEC
2022-01-302023-01-281,990,331,000USD2025-04-0110-K · 0000745732-25-000010 SEC

Related Ross Stores, Inc. histories

All 46 Ross Stores histories

Inspect the source

Entity
Ross Stores, Inc. / CIK 0000745732
Captured
SEC response SHA-256
6310bb034c7b7b228799f757b5117d1e6a173af3ff6304b656e851f492ccfaf9

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/0000745732.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"])))