LAM Research Corporation: current liabilities

At a glance

LAM Research reported current liabilities of $5.94 billion at 2026-06-28, down 9.6% from $6.57 billion a year earlier. From 2010 to 2026 it grew at a compound 15.9% a year, from $558.7 million to $5.94 billion. Across the last five reported years it ranged from $4.18 billion (2023-06-25) to $6.57 billion (2025-06-29). That was 25.2% of total assets ($23.5 billion) at the same date.

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

What this measure means

Obligations classified as current under the operating-cycle or one-year boundary. The balance includes more than short-term borrowing.

Exact concept: us-gaap:LiabilitiesCurrent. Each value is a balance at the reporting date, not a flow earned over a year. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2010-06-27 to 2026-06-28. The SEC response was captured on 2026-09-19.

Selected filing history

Related LAM RESEARCH CORPORATION histories

• Total liabilities
• Current accounts payable
• Accrued current liabilities
• Current contract liabilities
• Long-term debt
• Operating lease liability
• Total assets
• Stockholders equity

All 63 LAM Research histories

Inspect the source

Entity

LAM RESEARCH CORPORATION / CIK 0000707549

Captured

2026-09-19 14:58 UTC

SEC response SHA-256

5669585890b736938e937b858b2e62156a2cb4f3cb74845d8270eeee42689f90

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/0000707549.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"])))