Magic Software Enterprises Ltd.: net finite-lived intangible assets

At a glance

Magic Software Enterprises reported net finite-lived intangible assets of $51.4 million at 2021-12-31, down 3.8% from $53.4 million a year earlier. From 2011 to 2021 it grew at a compound 6.5% a year, from $27.4 million to $51.4 million. Across the last five reported years it ranged from $41.5 million (2018-12-31) to $53.4 million (2020-12-31). That was 10.4% of total assets ($492.0 million) 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

Finite-lived intangible assets after amortization. This excludes goodwill and should not be combined with indefinite-lived intangible assets without checking scope.

Exact concept: us-gaap:FiniteLivedIntangibleAssetsNet. 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 2011-12-31 to 2021-12-31. The SEC response was captured on 2026-09-20.

This selected history ends more than two years before capture. Do not treat its final value as a current balance or current annual result. More recent filings may use another accounting tag; inspect the filings before drawing conclusions about the company.

Selected filing history

Related MAGIC SOFTWARE ENTERPRISES LTD. histories

• Total assets
• Current assets
• Cash and cash equivalents
• Cash including restricted cash
• Net current accounts receivable
• Prepaid expenses and other current assets
• Stockholders equity
• Net income or loss

All 57 Magic Software Enterprises histories

Inspect the source

Entity

MAGIC SOFTWARE ENTERPRISES LTD. / CIK 0000876779

Captured

2026-09-20 05:01 UTC

SEC response SHA-256

f5ef7050ddbdf62c671bb421f057f22a363a80db50dc72569f2ddf764945c350

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/0000876779.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"])))