Joby Aviation (JOBY) Change in Accured Expenses (2020 - 2026)
Joby Aviation's Change in Accured Expenses came in at $7.9 million for Q2 2026, down 25.4% from $10.59 million a year earlier and down 84.3% from the prior quarter.
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Analysis
Joby Aviation (JOBY) Change in Accured Expenses (2020 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Joby Aviation reported Change in Accured Expenses of $39.16 million, up 218.6% year-over-year; for FY2025, it came in at -$6.9 million.
- Going back by year, Change in Accured Expenses was $6.12 million in FY2024 (-5.1%), $6.44 million in FY2023 (-40.8%), $10.88 million in FY2022 (+69.1%) and $6.44 million in FY2021 (-23.2%).
- The five-year range for quarterly Change in Accured Expenses is -$15.92 million (Q4 2025) to $50.22 million (Q1 2026).
- Year-over-year, Change in Accured Expenses increased in two of the last four quarters, with growth averaging 8.1%.
- The fastest year-over-year change in Change in Accured Expenses over five years came in Q2 2022 (growth of 248.5%), and the weakest in Q4 2024 (a decline of 88.3%).
- Business Quant data shows JOBY's Change in Accured Expenses at $50.22 million (Q1 2026), -$15.92 million (Q4 2025) and -$3.04 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 321.31 Bn | 275.31 Bn | 4.68 Bn | - |
| 2 | Rtx | 248.97 Bn | 222.19 Bn | 5.13 Bn | 2.10 Bn |
| 3 | Boeing | 152.99 Bn | 59.70 Bn | 2.41 Bn | 190.00 Mn |
| 4 | Lockheed Martin | 116.67 Bn | 103.39 Bn | 2.45 Bn | - |
| 5 | Howmet Aerospace | 92.54 Bn | 88.15 Bn | 951.00 Mn | 41.00 Mn |
| 6 | General Dynamics | 89.31 Bn | 76.42 Bn | 2.18 Bn | - |
| 7 | Motorola Solutions | 74.04 Bn | 70.40 Bn | 1.68 Bn | 82.00 Mn |
| 8 | Northrop Grumman | 67.90 Bn | 57.15 Bn | 2.12 Bn | -34.00 Mn |
| 9 | Honeywell International | 67.82 Bn | 20.27 Bn | 3.65 Bn | 895.00 Mn |
| 10 | Joby Aviation | 5.87 Bn | 5.87 Bn | - | 7.90 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 7.90 Mn |
| Mar 31, 2026 | 50.22 Mn |
| Dec 31, 2025 | -15.92 Mn |
| Sep 30, 2025 | -3.04 Mn |
| Jun 30, 2025 | 10.59 Mn |
| Mar 31, 2025 | 1.46 Mn |
| Dec 31, 2024 | 507,000.00 |
| Sep 30, 2024 | -269,000.00 |
| Jun 30, 2024 | 4.64 Mn |
| Mar 31, 2024 | 1.24 Mn |
| Dec 31, 2023 | 4.33 Mn |
| Sep 30, 2023 | -23,000.00 |
| Jun 30, 2023 | 3.22 Mn |
| Mar 31, 2023 | -1.08 Mn |
| Dec 31, 2022 | 8.87 Mn |
| Sep 30, 2022 | -1.05 Mn |
| Jun 30, 2022 | 6.06 Mn |
| Mar 31, 2022 | -3.00 Mn |
| Dec 31, 2021 | 6.87 Mn |
| Sep 30, 2021 | -2.19 Mn |
API Access
Joby Aviation Change in Accured Expenses 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=change-in-accured-expenses&ticker=JOBY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "JOBY", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=JOBY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();