Applied Energetics (AERG) Long-Term Debt Repayments (2018 - 2026)
Applied Energetics (AERG) posted Long-Term Debt Repayments of $69,446 for Q2 2026, up 334.0% from $16,000 a year earlier and up 44.7% from the prior quarter.
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Analysis
Applied Energetics (AERG) Long-Term Debt Repayments (2018 - 2026) Analysis & Trends
For FY2025, Applied Energetics' Long-Term Debt Repayments came in at $159,325, up 12.2% from FY2024.
- Annual Long-Term Debt Repayments shows a five-year compound annual growth rate of -35.0% (FY2020 to FY2025).
- In prior years, Applied Energetics' Long-Term Debt Repayments was $141,977 in FY2024 (-74.4%), $555,541 in FY2023 (-30.5%), $798,988 in FY2022 (-51.5%) and $1.65 million in FY2021 (+19.9%).
- The Q2 2026 figure stands as the highest quarterly Long-Term Debt Repayments since Q2 2023.
- On a year-over-year basis, Long-Term Debt Repayments has increased in each of the last four quarters, with growth averaging 53.5% over the last six quarters.
- The strongest year-over-year quarter for Long-Term Debt Repayments in the past five years was Q3 2022, with growth of 663.8%; the weakest was Q1 2022, with a decline of 96.6%.
- According to Business Quant data, Long-Term Debt Repayments for the three prior quarters was $48,000 (Q1 2026), $48,000 (Q4 2025) and $48,000 (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | LT Debt Repayment (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 334.86 Bn | 293.38 Bn | 5.02 Bn | 248.00 Mn |
| 2 | Rtx | 256.39 Bn | 224.66 Bn | 5.81 Bn | 2.00 Mn |
| 3 | Boeing | 137.52 Bn | 46.48 Bn | 2.17 Bn | 39.00 Mn |
| 4 | Lockheed Martin | 130.55 Bn | 114.79 Bn | 2.44 Bn | - |
| 5 | General Dynamics | 95.65 Bn | 80.88 Bn | 2.25 Bn | 500.00 Mn |
| 6 | Motorola Solutions | 85.05 Bn | 81.51 Bn | 1.77 Bn | - |
| 7 | Howmet Aerospace | 81.91 Bn | 77.67 Bn | 1.00 Bn | - |
| 8 | Northrop Grumman | 74.74 Bn | 62.20 Bn | 2.35 Bn | - |
| 9 | Honeywell International | 69.91 Bn | 29.43 Bn | 2.02 Bn | 1.50 Bn |
| 10 | Applied Energetics | 195.41 Mn | 182.39 Mn | 40,430.00 | 69,446.00 |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 69,446.00 |
| Mar 31, 2026 | 48,000.00 |
| Dec 31, 2025 | 48,000.00 |
| Sep 30, 2025 | 48,000.00 |
| Jun 30, 2025 | 16,000.00 |
| Mar 31, 2025 | 47,325.00 |
| Dec 31, 2024 | 47,326.00 |
| Sep 30, 2024 | 47,326.00 |
| Dec 31, 2023 | 51,847.00 |
| Sep 30, 2023 | 51,847.00 |
| Jun 30, 2023 | 151,847.00 |
| Mar 31, 2023 | 300,000.00 |
| Dec 31, 2022 | 338,986.00 |
| Sep 30, 2022 | 358,478.00 |
| Jun 30, 2022 | 84,104.00 |
| Mar 31, 2022 | 17,420.00 |
| Dec 31, 2021 | 544,400.00 |
| Sep 30, 2021 | 50,020.00 |
| Jun 30, 2021 | 539,070.00 |
| Mar 31, 2021 | 513,023.00 |
API Access
Applied Energetics Long-Term Debt Repayments 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=long-term-debt-repayments&ticker=AERG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "long-term-debt-repayments", "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=long-term-debt-repayments&ticker=AERG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();