Skip to content

Abbott Laboratories: current liabilities

Abbott Laboratories current liabilities: $16.5 billion at 2025-12-31, up 16.5% on the year. 19 values since 2007 from SEC filings, each linked to its filing.

All Abbott Laboratories financial histories

At a glance

Abbott Laboratories reported current liabilities of $16.5 billion at 2025-12-31, up 16.5% from $14.2 billion a year earlier. From 2007 to 2025 it grew at a compound 3.4% a year, from $9.10 billion to $16.5 billion. Across the last five reported years it ranged from $13.1 billion (2021-12-31) to $16.5 billion (2025-12-31). That was 19.0% of total assets ($86.7 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

Obligations classified as current under the operating-cycle or one-year boundary. The balance includes more than short-term borrowing.

Exact concept: us-gaap:LiabilitiesCurrent. 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 2007-12-31 to 2025-12-31. The SEC response was captured on 2026-09-19.

Selected filing history

Current liabilities in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
-2025-12-3116,496,000,000USD2026-02-2010-K · 0001628280-26-010185 SEC
-2024-12-3114,157,000,000USD2026-02-2010-K · 0001628280-26-010185 SEC
-2023-12-3113,841,000,000USD2025-02-2110-K · 0001628280-25-007110 SEC
-2022-12-3115,489,000,000USD2024-02-1610-K · 0001628280-24-005348 SEC
-2021-12-3113,105,000,000USD2023-02-1710-K · 0001628280-23-004026 SEC
-2020-12-3111,907,000,000USD2022-02-1810-K · 0001104659-22-025141 SEC
-2019-12-3110,863,000,000USD2021-02-1910-K · 0001104659-21-025751 SEC
-2018-12-319,012,000,000USD2020-02-2110-K · 0001104659-20-023904 SEC
-2017-12-318,912,000,000USD2019-02-2210-K · 0001047469-19-000624 SEC
-2016-12-316,660,000,000USD2018-02-1610-K · 0001047469-18-000856 SEC
-2015-12-319,186,000,000USD2017-02-1710-K · 0001047469-17-000744 SEC
-2014-12-3110,467,000,000USD2016-02-1910-K · 0001047469-16-010246 SEC
-2013-12-319,507,000,000USD2015-02-2710-K · 0001047469-15-001377 SEC
-2012-12-3113,280,000,000USD2014-02-2110-K · 0001047469-14-001176 SEC
-2011-12-3115,480,228,000USD2013-02-1510-K · 0001047469-13-001180 SEC
-2010-12-3117,262,434,000USD2013-02-1510-K · 0001047469-13-001180 SEC
-2009-12-3113,049,489,000USD2012-02-2110-K · 0001047469-12-001216 SEC
-2008-12-3111,591,908,000USD2011-02-1810-K · 0001047469-11-001056 SEC
-2007-12-319,103,278,000USD2010-02-1910-K · 0001047469-10-001018 SEC

Related ABBOTT LABORATORIES histories

All 54 Abbott Laboratories histories

Inspect the source

Entity
ABBOTT LABORATORIES / CIK 0000001800
Captured
SEC response SHA-256
5d751f73769f5c0a1f9bee0de16cb39d8961cf3f1ebb5558d555bb2678afb4ff

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/0000001800.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"])))