Femasys (FEMY) Total Current Liabilities (2020 - 2026)
Femasys' Total Current Liabilities was $3.89 million in Q2 2026, down 64.5% from $10.96 million a year earlier but up 23.2% from the prior quarter.
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Analysis
Femasys (FEMY) Total Current Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, Total Current Liabilities at Femasys came in at $3.64 million, down 57.6% from FY2024.
- Total Current Liabilities shows a five-year compound annual growth rate of 4.7% (FY2020 to FY2025).
- In earlier years, Total Current Liabilities was $8.58 million in FY2024 (+181.0%), $3.05 million in FY2023 (+99.9%), $1.53 million in FY2022 (-9.7%) and $1.69 million in FY2021 (-41.4%).
- Quarterly Total Current Liabilities has moved between $1.38 million (Q2 2023) and $10.96 million (Q2 2025) over five years.
- Compared with a year earlier, Total Current Liabilities has declined for three straight quarters, with growth averaging 119.0% over the last eight quarters.
- The best year-over-year quarter for Total Current Liabilities over five years was Q2 2025 (growth of 345.0%); the worst was Q1 2026 (a decline of 67.9%).
- Per Business Quant data, FEMY's Total Current Liabilities in the three quarters before Q2 2026 was $3.16 million (Q1 2026), $3.64 million (Q4 2025) and $10.54 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn | 15.07 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn | 17.81 Bn |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn | 8.11 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn | 1.92 Bn |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn | 11.80 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn | 6.68 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn | 6.43 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn | 1.51 Bn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn | 9.40 Bn |
| 10 | Femasys | 6.32 Mn | -14.34 Mn | 189,391.00 | 3.89 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 3.89 Mn |
| Mar 31, 2026 | 3.16 Mn |
| Dec 31, 2025 | 3.64 Mn |
| Sep 30, 2025 | 10.54 Mn |
| Jun 30, 2025 | 10.96 Mn |
| Mar 31, 2025 | 9.84 Mn |
| Dec 31, 2024 | 8.58 Mn |
| Sep 30, 2024 | 2.87 Mn |
| Jun 30, 2024 | 2.46 Mn |
| Mar 31, 2024 | 2.36 Mn |
| Dec 31, 2023 | 3.05 Mn |
| Sep 30, 2023 | 2.19 Mn |
| Jun 30, 2023 | 1.38 Mn |
| Mar 31, 2023 | 1.41 Mn |
| Dec 31, 2022 | 1.53 Mn |
| Sep 30, 2022 | 1.81 Mn |
| Jun 30, 2022 | 1.86 Mn |
| Mar 31, 2022 | 1.63 Mn |
| Dec 31, 2021 | 1.69 Mn |
| Sep 30, 2021 | 1.82 Mn |
API Access
Femasys Total Current Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-current-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-current-liabilities", "ticker": "FEMY", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=total-current-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();