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Cal-Maine Foods, Inc.: share-based compensation expense

Cal-Maine Foods share-based compensation expense: $5.76 million for the year ending 2026-05-30, up 27.2% on the year. 8 values since 2019 from SEC filings.

All Cal-Maine Foods, Inc. financial histories

At a glance

Cal-Maine Foods reported share-based compensation expense of $5.76 million for the year ending 2026-05-30, up 27.2% from $4.53 million a year earlier. From 2019 to 2026 it grew at a compound 6.9% a year, from $3.62 million to $5.76 million. Across the last five reported years it ranged from $4.06 million (2022-05-28) to $5.76 million (2026-05-30). That was 0.2% of revenue ($2.91 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

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 2018-06-03 to 2026-05-30. The SEC response was captured on 2026-09-19.

Selected filing history

Share-based compensation expense in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-06-012026-05-305,757,000USD2026-07-2210-K · 0001562762-26-000080 SEC
2024-06-022025-05-314,527,000USD2026-07-2210-K · 0001562762-26-000080 SEC
2023-06-042024-06-014,358,000USD2026-07-2210-K · 0001562762-26-000080 SEC
2022-05-292023-06-034,205,000USD2025-07-2210-K · 0001562762-25-000170 SEC
2021-05-302022-05-284,063,000USD2024-07-2310-K · 0001562762-24-000177 SEC
2020-05-312021-05-293,778,000USD2023-07-2510-K · 0001562762-23-000287 SEC
2019-06-022020-05-303,617,000USD2022-07-1910-K · 0001562762-22-000297 SEC
2018-06-032019-06-013,619,000USD2021-07-1910-K · 0001562762-21-000265 SEC

Related Cal-Maine Foods, Inc. histories

All 63 Cal-Maine Foods histories

Inspect the source

Entity
Cal-Maine Foods, Inc. / CIK 0000016160
Captured
SEC response SHA-256
7364d066b9d47d16e028c86d85c534dae87e099150d26aad56f301119b026c87

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.

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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/0000016160.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"])))