Lowes Companies Inc: operating expenses

At a glance

Lowes Companies reported operating expenses of $16.2 billion for the year ending 2016-01-29, up 5.7% from $15.3 billion a year earlier. From 2008 to 2016 it grew at a compound 3.6% a year, from $12.2 billion to $16.2 billion. Across the last five reported years it ranged from $14.2 billion (2013-02-01) to $16.2 billion (2016-01-29).

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

What this measure means

Recurring operating costs under this accounting concept, generally excluding production costs included in cost of sales. Check filing presentation before combining expense subtotals.

Exact concept: us-gaap:OperatingExpenses. 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 2007-02-03 to 2016-01-29. The SEC response was captured on 2026-09-19.

This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.

Selected filing history

Related LOWES COMPANIES INC histories

• Contract revenue excluding tax
• Gross profit
• Selling, general and administrative expense
• Operating income or loss
• Interest expense
• Pre-tax income or loss from continuing operations
• Total assets
• Total liabilities

All 55 Lowes Companies histories

Inspect the source

Entity

LOWES COMPANIES INC / CIK 0000060667

Captured

2026-09-19 14:48 UTC

SEC response SHA-256

7912551d61433f5e7e863ac602ebf24ee1484699e10aa556534f0055fff2ea7d

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.

• Get an API key and run your first validation
• Connect the MCP server to your coding assistant
• Inspect the ALPHAC engine on GitHub
• Read the MCP server source and integration examples

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.

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000060667.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"])))