Vishay Precision (VPG) Total Liabilities (2010 - 2026)
Vishay Precision (VPG) recorded Total Liabilities of $117.97 million in Q2 2026, down 12.2% from $134.3 million a year earlier and down 1.2% from the prior quarter.
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Analysis
Vishay Precision (VPG) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025, Vishay Precision reported Total Liabilities of $119.52 million, down 7.4% from FY2024.
- Annual Total Liabilities has declined for four straight years, with a five-year compound annual growth rate of -3.7% (FY2020 to FY2025).
- Across earlier years, Total Liabilities came in at $129.08 million in FY2024 (-8.9%), $141.64 million in FY2023 (-16.8%), $170.22 million in FY2022 (-7.9%) and $184.85 million in FY2021 (+28.3%).
- The Q2 2026 figure is the lowest quarterly Total Liabilities since Q3 2019.
- On a year-over-year basis, Total Liabilities has declined for 17 consecutive quarters, with an average decline of 8.4% over the last eight quarters.
- Peak year-over-year performance for Total Liabilities in the last five years was growth of 28.6% in Q3 2021, against a decline of 17.9% in Q2 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $119.36 million (Q1 2026), $119.52 million (Q4 2025) and $124.52 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Caterpillar | 372.79 Bn | 344.49 Bn | 7.76 Bn | 83.22 Bn |
| 2 | Amphenol | 207.98 Bn | 202.68 Bn | 3.55 Bn | 29.19 Bn |
| 3 | Deere | 181.02 Bn | 189.78 Bn | 4.66 Bn | 79.61 Bn |
| 4 | Eaton | 166.86 Bn | 164.09 Bn | 2.86 Bn | 35.88 Bn |
| 5 | Parker-Hannifin | 120.45 Bn | 118.58 Bn | 2.25 Bn | 15.46 Bn |
| 6 | Vertiv Holdings | 92.94 Bn | 83.56 Bn | 1.23 Bn | 11.14 Bn |
| 7 | Emerson Electric | 86.56 Bn | 79.32 Bn | 2.66 Bn | 21.81 Bn |
| 8 | 3M | 84.72 Bn | 66.18 Bn | 2.68 Bn | 31.92 Bn |
| 9 | Illinois Tool Works | 73.32 Bn | 69.88 Bn | 1.90 Bn | 13.60 Bn |
| 10 | Vishay Precision | 1.86 Bn | 1.53 Bn | 32.44 Mn | 117.97 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 4, 2026 | 117.97 Mn |
| Apr 4, 2026 | 119.36 Mn |
| Dec 31, 2025 | 119.52 Mn |
| Sep 27, 2025 | 124.52 Mn |
| Jun 28, 2025 | 134.30 Mn |
| Mar 29, 2025 | 128.83 Mn |
| Dec 31, 2024 | 129.08 Mn |
| Sep 28, 2024 | 131.87 Mn |
| Jun 29, 2024 | 135.76 Mn |
| Mar 30, 2024 | 138.33 Mn |
| Dec 31, 2023 | 141.64 Mn |
| Sep 30, 2023 | 160.15 Mn |
| Jul 1, 2023 | 165.31 Mn |
| Apr 1, 2023 | 165.88 Mn |
| Dec 31, 2022 | 170.22 Mn |
| Oct 1, 2022 | 167.70 Mn |
| Jul 2, 2022 | 173.03 Mn |
| Apr 2, 2022 | 175.98 Mn |
| Dec 31, 2021 | 184.85 Mn |
| Oct 2, 2021 | 180.26 Mn |
API Access
Vishay Precision 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=VPG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "VPG", "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=VPG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();