Femasys (FEMY) Total Liabilities (2020 - 2026)
Femasys' Total Liabilities was $8.26 million in Q2 2026, down 32.7% from $12.27 million a year earlier and down 10.3% from the prior quarter.
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Analysis
Femasys (FEMY) Total Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, Total Liabilities at Femasys came in at $14.85 million, up 46.5% from FY2024.
- Total Liabilities has now increased for three consecutive years, though with a five-year compound annual growth rate of -24.2% (FY2020 to FY2025).
- In earlier years, Total Liabilities was $10.14 million in FY2024 (+7.8%), $9.4 million in FY2023 (+468.8%), $1.65 million in FY2022 (-26.3%) and $2.24 million in FY2021 (-96.2%).
- The Q2 2026 figure marks the lowest quarterly Total Liabilities since Q3 2023.
- Compared with a year earlier, Total Liabilities was higher in six of the last eight quarters, with growth averaging 26.0%.
- The best year-over-year quarter for Total Liabilities over five years was Q2 2024 (growth of 527.8%); the worst was Q4 2021 (a decline of 96.2%).
- Per Business Quant data, FEMY's Total Liabilities in the three quarters before Q2 2026 was $9.21 million (Q1 2026), $14.85 million (Q4 2025) and $11.73 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn | 60.49 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn | 57.45 Bn |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn | 39.78 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn | 2.58 Bn |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn | 42.46 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn | 23.94 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn | 20.04 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn | 3.14 Bn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn | 26.32 Bn |
| 10 | Femasys | 6.32 Mn | -14.34 Mn | 189,391.00 | 8.26 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 8.26 Mn |
| Mar 31, 2026 | 9.21 Mn |
| Dec 31, 2025 | 14.85 Mn |
| Sep 30, 2025 | 11.73 Mn |
| Jun 30, 2025 | 12.27 Mn |
| Mar 31, 2025 | 11.28 Mn |
| Dec 31, 2024 | 10.14 Mn |
| Sep 30, 2024 | 9.62 Mn |
| Jun 30, 2024 | 9.03 Mn |
| Mar 31, 2024 | 8.76 Mn |
| Dec 31, 2023 | 9.40 Mn |
| Sep 30, 2023 | 4.41 Mn |
| Jun 30, 2023 | 1.44 Mn |
| Mar 31, 2023 | 1.51 Mn |
| Dec 31, 2022 | 1.65 Mn |
| Sep 30, 2022 | 2.03 Mn |
| Jun 30, 2022 | 2.18 Mn |
| Mar 31, 2022 | 2.04 Mn |
| Dec 31, 2021 | 2.24 Mn |
| Sep 30, 2021 | 2.51 Mn |
API Access
Femasys Total 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-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-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-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();