Femasys (FEMY) Total Non-Current Liabilities (2020 - 2026)
Femasys (FEMY) reported Total Non-Current Liabilities of $4.37 million for Q2 2026, up 233.9% from $1.31 million a year earlier but down 27.7% from the prior quarter.
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Analysis
Femasys (FEMY) Total Non-Current Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, Femasys posted Total Non-Current Liabilities of $11.22 million, up 620.2% from FY2024.
- Total Non-Current Liabilities has a five-year compound annual growth rate of 56.6% (FY2020 to FY2025).
- By year, Total Non-Current Liabilities came in at $1.56 million in FY2024 (-75.5%), $6.35 million in FY2023, $125,242 in FY2022 (-77.3%) and $552,208 in FY2021 (-53.6%).
- Five-year quarterly Total Non-Current Liabilities spans a low of $56,245 in Q2 2023 and a high of $11.22 million in Q4 2025.
- Year over year, Total Non-Current Liabilities has now increased in each of the last three quarters, with growth averaging 132.8% over the last eight quarters.
- The high point for year-over-year Total Non-Current Liabilities in five years was Q3 2023 (growth of 921.7%); the low point was Q3 2025 (a decline of 82.3%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $6.05 million (Q1 2026), $11.22 million (Q4 2025) and $1.19 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn | 55.95 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn | - |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn | 33.25 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn | - |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn | 40.46 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn | 21.28 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn | 17.56 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn | 2.57 Bn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn | 23.92 Bn |
| 10 | Femasys | 6.32 Mn | -14.34 Mn | 189,391.00 | 4.37 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 4.37 Mn |
| Mar 31, 2026 | 6.05 Mn |
| Dec 31, 2025 | 11.22 Mn |
| Sep 30, 2025 | 1.19 Mn |
| Jun 30, 2025 | 1.31 Mn |
| Mar 31, 2025 | 1.44 Mn |
| Dec 31, 2024 | 1.56 Mn |
| Sep 30, 2024 | 6.75 Mn |
| Jun 30, 2024 | 6.56 Mn |
| Mar 31, 2024 | 6.41 Mn |
| Dec 31, 2023 | 6.35 Mn |
| Sep 30, 2023 | 2.22 Mn |
| Jun 30, 2023 | 56,245.00 |
| Mar 31, 2023 | 98,818.00 |
| Dec 31, 2022 | 125,242.00 |
| Sep 30, 2022 | 217,577.00 |
| Jun 30, 2022 | 318,237.00 |
| Mar 31, 2022 | 407,573.00 |
| Dec 31, 2021 | 552,208.00 |
| Sep 30, 2021 | 690,767.00 |
API Access
Femasys Total Non-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-non-current-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-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-non-current-liabilities&ticker=FEMY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();