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Agilysys, Inc.: share-based compensation expense

Agilysys share-based compensation expense: $21.8 million for the year ending 2026-03-31, up 22.7% on the year. 17 values since 2010. In original units.

All Agilysys, Inc. financial histories

At a glance

Agilysys reported share-based compensation expense of $21.8 million for the year ending 2026-03-31, up 22.7% from $17.8 million a year earlier. From 2010 to 2026 it grew at a compound 16.3% a year, from $1.95 million to $21.8 million. Across the last five reported years it ranged from $13.0 million (2023-03-31) to $21.8 million (2026-03-31). That was 6.8% of revenue ($319.3 million) 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 2009-04-01 to 2026-03-31. 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
2025-04-012026-03-3121,818,000USD2026-05-2110-K · 0001193125-26-234423 SEC
2024-04-012025-03-3117,777,000USD2026-05-2110-K · 0001193125-26-234423 SEC
2023-04-012024-03-3114,111,000USD2026-05-2110-K · 0001193125-26-234423 SEC
2022-04-012023-03-3112,958,000USD2025-05-2310-K · 0000950170-25-076527 SEC
2021-04-012022-03-3114,549,000USD2024-05-2210-K · 0000950170-24-063222 SEC
2020-04-012021-03-3140,093,000USD2023-05-1910-K · 0000950170-23-023196 SEC
2019-04-012020-03-315,205,000USD2022-05-2310-K · 0000950170-22-010512 SEC
2018-04-012019-03-314,376,000USD2021-05-2110-K · 0001564590-21-029319 SEC
2017-04-012018-03-314,688,000USD2020-05-2210-K · 0001564590-20-026737 SEC
2016-04-012017-03-312,427,000USD2019-05-2410-K · 0000078749-19-000021 SEC
2015-04-012016-03-313,405,000USD2018-05-2510-K · 0000078749-18-000022 SEC
2014-04-012015-03-313,140,000USD2017-06-0210-K · 0000078749-17-000021 SEC
2013-04-012014-03-312,119,000USD2016-06-1010-K · 0000078749-16-000080 SEC
2012-04-012013-03-311,638,000USD2016-02-0910-K · 0000078749-16-000068 SEC
2011-04-012012-03-312,397,000USD2014-06-0410-K · 0000078749-14-000013 SEC
2010-04-012011-03-312,812,000USD2013-06-1410-K · 0000078749-13-000023 SEC
2009-04-012010-03-311,947,000USD2012-06-1210-K · 0001193125-12-268267 SEC

Related AGILYSYS, INC. histories

All 59 Agilysys histories

Inspect the source

Entity
AGILYSYS, INC. / CIK 0000078749
Captured
SEC response SHA-256
3cc527a4dfed053c330508eb9e2a7b5c3dafbe32b8367962d4a94969678bd55d

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.

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/0000078749.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"])))