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Laureate Education, Inc.: cash and cash equivalents

Laureate Education cash and cash equivalents: $146.7 million at 2025-12-31, up 60.6% on the year. 12 values since 2014, each linked to its SEC filing.

All Laureate Education, Inc. financial histories

At a glance

Laureate Education reported cash and cash equivalents of $146.7 million at 2025-12-31, up 60.6% from $91.3 million a year earlier. From 2014 to 2025 it fell at a compound 9.9% a year, from $461.6 million to $146.7 million. Across the last five reported years it ranged from $85.2 million (2022-12-31) to $324.8 million (2021-12-31). That was 6.6% of total assets ($2.21 billion) 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

Cash and qualifying short-term liquid investments under the filer’s accounting policy. Restricted cash and longer-term investments may be reported separately.

Exact concept: us-gaap:CashAndCashEquivalentsAtCarryingValue. 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 2014-12-31 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Cash and cash equivalents in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
-2025-12-31146,703,000USD2026-02-1910-K · 0001628280-26-009479 SEC
-2024-12-3191,350,000USD2026-02-1910-K · 0001628280-26-009479 SEC
-2023-12-3189,392,000USD2026-02-1910-K · 0001628280-26-009479 SEC
-2022-12-3185,167,000USD2025-02-2010-K · 0001628280-25-006557 SEC
-2021-12-31324,801,000USD2024-02-2210-K · 0001628280-24-006141 SEC
-2020-12-31750,147,000USD2023-02-2310-K · 0001628280-23-004686 SEC
-2019-12-3161,576,000USD2022-02-2410-K · 0001628280-22-003638 SEC
-2018-12-3177,765,000USD2021-02-2510-K · 0001628280-21-003163 SEC
-2017-12-31319,040,000USD2020-02-2710-K · 0001628280-20-002350 SEC
-2016-12-31295,785,000USD2019-02-2810-K · 0001047469-19-000767 SEC
-2015-12-31458,673,000USD2018-03-2010-K · 0001047469-18-001893 SEC
-2014-12-31461,584,000USD2018-03-2010-K · 0001047469-18-001893 SEC

Related Laureate Education, Inc. histories

All 53 Laureate Education histories

Inspect the source

Entity
Laureate Education, Inc. / CIK 0000912766
Captured
SEC response SHA-256
b16b40f41a476c7331ea7488f76dc5b1748bcff58c8a9897da4b8892ff93c8ef

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/0000912766.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"])))