LAM RESEARCH CORPORATION: share-based compensation expense

What this measure means

Reported noncash expense for share-based payment arrangements. Noncash treatment does not mean the awards have no economic cost to shareholders.

Exact concept: us-gaap:ShareBasedCompensation. 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 2008-06-30 to 2026-06-28. The SEC response was captured on 2026-09-19.

Selected filing history

Related financial histories

• LAM RESEARCH CORPORATION: total assets
• LAM RESEARCH CORPORATION: total liabilities
• LAM RESEARCH CORPORATION: stockholders equity
• LAM RESEARCH CORPORATION: cash and cash equivalents
• LAM RESEARCH CORPORATION: net income or loss
• LAM RESEARCH CORPORATION: operating cash flow
• LAM RESEARCH CORPORATION: revenue
• LAM RESEARCH CORPORATION: contract revenue excluding tax
• LAM RESEARCH CORPORATION: financing cash flow
• LAM RESEARCH CORPORATION: investing cash flow
• LAM RESEARCH CORPORATION: retained earnings or deficit
• LAM RESEARCH CORPORATION: basic weighted-average shares
• LAM RESEARCH CORPORATION: diluted weighted-average shares
• LAM RESEARCH CORPORATION: basic earnings per share
• LAM RESEARCH CORPORATION: diluted earnings per share
• LAM RESEARCH CORPORATION: income tax expense or benefit
• LAM RESEARCH CORPORATION: net property, plant and equipment
• LAM RESEARCH CORPORATION: operating income or loss
• LAM RESEARCH CORPORATION: current assets
• LAM RESEARCH CORPORATION: interest expense
• LAM RESEARCH CORPORATION: current liabilities
• LAM RESEARCH CORPORATION: current accounts payable
• LAM RESEARCH CORPORATION: goodwill carrying amount
• LAM RESEARCH CORPORATION: net finite-lived intangible assets
• LAM RESEARCH CORPORATION: net current accounts receivable
• LAM RESEARCH CORPORATION: common-stock repurchase payments
• LAM RESEARCH CORPORATION: operating expenses
• LAM RESEARCH CORPORATION: net inventory
• LAM RESEARCH CORPORATION: gross profit
• LAM RESEARCH CORPORATION: cost of revenue
• LAM RESEARCH CORPORATION: selling, general and administrative expense
• LAM RESEARCH CORPORATION: research and development expense

Inspect the source

Entity

LAM RESEARCH CORPORATION / CIK 0000707549

Captured

2026-09-19T14:58:12.354Z

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

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"])))