3D Systems (DDD) Total Liabilities (2010 - 2026)
3D Systems (DDD) posted Total Liabilities of $261.68 million for Q2 2026, down 24.5% from $346.6 million a year earlier and down 6.1% from the prior quarter.
3D Systems (DDD) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025, 3D Systems' Total Liabilities came in at $281.37 million, down 35.0% from FY2024.
- Annual Total Liabilities has declined for four consecutive years, with a five-year compound annual growth rate of -1.4% (FY2020 to FY2025).
- In prior years, 3D Systems' Total Liabilities was $432.65 million in FY2024 (-23.3%), $563.91 million in FY2023 (-19.1%), $696.67 million in FY2022 (-1.4%) and $706.72 million in FY2021 (+133.8%).
- The Q2 2026 figure stands as the lowest quarterly Total Liabilities since Q3 2021.
- On a year-over-year basis, Total Liabilities has declined in each of the last 12 quarters, with an average decline of 25.4% over the last eight quarters.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q2 2022, with growth of 184.1%; the weakest was Q2 2024, with a decline of 39.0%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $278.77 million (Q1 2026), $281.37 million (Q4 2025) and $333.45 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Caterpillar | 339.35 Bn | 311.04 Bn | 7.76 Bn | 83.22 Bn |
| 2 | Amphenol | 197.18 Bn | 191.76 Bn | 3.55 Bn | 29.19 Bn |
| 3 | Deere | 170.42 Bn | 138.52 Bn | 4.66 Bn | 79.61 Bn |
| 4 | Eaton | 152.64 Bn | 149.82 Bn | 2.86 Bn | 35.88 Bn |
| 5 | Parker-Hannifin | 120.68 Bn | 118.80 Bn | 2.25 Bn | 15.46 Bn |
| 6 | Emerson Electric | 90.56 Bn | 83.30 Bn | 2.66 Bn | 21.81 Bn |
| 7 | 3M | 89.11 Bn | 70.51 Bn | 2.68 Bn | 31.92 Bn |
| 8 | Vertiv Holdings | 83.00 Bn | 73.62 Bn | 1.23 Bn | 11.14 Bn |
| 9 | Illinois Tool Works | 79.20 Bn | 75.76 Bn | 1.90 Bn | 13.60 Bn |
| 10 | 3D Systems | 455.10 Mn | 50.89 Mn | 34.46 Mn | 261.68 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 261.68 Mn |
| Mar 31, 2026 | 278.77 Mn |
| Dec 31, 2025 | 281.37 Mn |
| Sep 30, 2025 | 333.45 Mn |
| Jun 30, 2025 | 346.60 Mn |
| Mar 31, 2025 | 437.72 Mn |
| Dec 31, 2024 | 432.65 Mn |
| Sep 30, 2024 | 440.24 Mn |
| Jun 30, 2024 | 439.76 Mn |
| Mar 31, 2024 | 448.87 Mn |
| Dec 31, 2023 | 563.91 Mn |
| Sep 30, 2023 | 693.22 Mn |
| Jun 30, 2023 | 720.69 Mn |
| Mar 31, 2023 | 712.61 Mn |
| Dec 31, 2022 | 696.67 Mn |
| Sep 30, 2022 | 697.11 Mn |
| Jun 30, 2022 | 698.30 Mn |
| Mar 31, 2022 | 679.69 Mn |
| Dec 31, 2021 | 706.72 Mn |
| Sep 30, 2021 | 255.95 Mn |
3D Systems 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=DDD&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "DDD", "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=DDD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();