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Monster Beverage Corp: share-based compensation expense

Share-based compensation expense for Monster Beverage Corp. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Monster Beverage Corp financial histories

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 2008-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Share-based compensation expense in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31125,687,000USD2026-02-2710-K · 0001104659-26-020831
2024-01-012024-12-3190,985,000USD2026-02-2710-K · 0001104659-26-020831
2023-01-012023-12-3168,836,000USD2026-02-2710-K · 0001104659-26-020831
2022-01-012022-12-3164,109,000USD2025-02-2810-K · 0001410578-25-000248
2021-01-012021-12-3170,483,000USD2024-02-2910-K · 0001104659-24-029425
2020-01-012020-12-3170,289,000USD2023-03-0110-K · 0001104659-23-027245
2019-01-012019-12-3163,356,000USD2022-02-2810-K · 0001104659-22-028182
2018-01-012018-12-3157,111,000USD2021-03-0110-K · 0001104659-21-029943
2017-01-012017-12-3152,282,000USD2020-02-2810-K · 0001104659-20-027209
2016-01-012016-12-3145,848,000USD2019-02-2810-K · 0001104659-19-011581
2015-01-012015-12-3132,719,000USD2018-03-0110-K · 0001104659-18-014057
2014-01-012014-12-3128,552,000USD2017-03-0110-K · 0001104659-17-013048
2013-01-012013-12-3128,764,000USD2016-02-2910-K · 0001104659-16-100960
2012-01-012012-12-3128,413,000USD2015-03-0210-K · 0001104659-15-015731
2011-01-012011-12-3119,424,000USD2014-03-0310-K · 0001104659-14-015320
2010-01-012010-12-3116,862,000USD2013-03-0110-K · 0001104659-13-016713
2009-01-012009-12-3114,040,000USD2012-02-2910-K · 0001104659-12-014573
2008-01-012008-12-3113,899,000USD2011-03-0110-K · 0001104659-11-011438

Related financial histories

Inspect the source

Entity
Monster Beverage Corp / CIK 0000865752
Captured
2026-09-20T05:00:02.209Z
SEC response SHA-256
64144475e8bf6500e75b335c46c775c2f1f9d1c8e708b9d71f43f1d6eb3eed07

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.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000865752.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"])))