Applied Energetics (AERG) Total Liabilities (2010 - 2026)
Applied Energetics (AERG) posted Total Liabilities of $1.88 million for Q2 2026, up 2.8% from $1.83 million a year earlier and up 24.5% from the prior quarter.
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Analysis
Applied Energetics (AERG) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025, Applied Energetics' Total Liabilities came in at $1.5 million, down 10.3% from FY2024.
- Annual Total Liabilities shows a five-year compound annual growth rate of -12.5% (FY2020 to FY2025).
- In prior years, Applied Energetics' Total Liabilities was $1.68 million in FY2024 (-12.7%), $1.92 million in FY2023 (+67.1%), $1.15 million in FY2022 (-40.2%) and $1.92 million in FY2021 (-34.4%).
- Quarterly Total Liabilities has run from a low of $1.09 million in Q1 2023 to a high of $4.75 million in Q3 2025 over five years.
- On a year-over-year basis, Total Liabilities increased in three of the last eight quarters, with growth averaging 10.6%.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q1 2024, with growth of 184.3%; the weakest was Q3 2021, with a decline of 75.8%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $1.51 million (Q1 2026), $1.5 million (Q4 2025) and $4.75 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 312.56 Bn | 266.33 Bn | 4.68 Bn | 109.81 Bn |
| 2 | Rtx | 262.07 Bn | 233.55 Bn | 5.13 Bn | 105.86 Bn |
| 3 | Boeing | 150.53 Bn | 57.22 Bn | 2.41 Bn | 159.76 Bn |
| 4 | Lockheed Martin | 128.14 Bn | 114.86 Bn | 2.45 Bn | 53.68 Bn |
| 5 | General Dynamics | 93.37 Bn | 80.49 Bn | 2.18 Bn | 33.34 Bn |
| 6 | Howmet Aerospace | 85.79 Bn | 81.39 Bn | 951.00 Mn | 7.52 Bn |
| 7 | Motorola Solutions | 76.59 Bn | 72.94 Bn | 1.68 Bn | 16.55 Bn |
| 8 | Northrop Grumman | 76.28 Bn | 65.52 Bn | 2.12 Bn | 32.88 Bn |
| 9 | Honeywell International | 66.74 Bn | 18.86 Bn | 3.65 Bn | 58.49 Bn |
| 10 | Applied Energetics | 179.48 Mn | 166.46 Mn | 40,430.00 | 1.88 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 1.88 Mn |
| Mar 31, 2026 | 1.51 Mn |
| Dec 31, 2025 | 1.50 Mn |
| Sep 30, 2025 | 4.75 Mn |
| Jun 30, 2025 | 1.83 Mn |
| Mar 31, 2025 | 1.64 Mn |
| Dec 31, 2024 | 1.68 Mn |
| Sep 30, 2024 | 1.91 Mn |
| Jun 30, 2024 | 1.59 Mn |
| Mar 31, 2024 | 3.10 Mn |
| Dec 31, 2023 | 1.92 Mn |
| Sep 30, 2023 | 1.99 Mn |
| Jun 30, 2023 | 1.12 Mn |
| Mar 31, 2023 | 1.09 Mn |
| Dec 31, 2022 | 1.15 Mn |
| Sep 30, 2022 | 2.21 Mn |
| Jun 30, 2022 | 1.91 Mn |
| Mar 31, 2022 | 2.01 Mn |
| Dec 31, 2021 | 1.92 Mn |
| Sep 30, 2021 | 2.39 Mn |
API Access
Applied Energetics 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=AERG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "AERG", "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=AERG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();