AvePoint (AVPT) Change in Accured Expenses (2019 - 2026)
AvePoint's Change in Accured Expenses came in at $11.58 million for Q2 2026, up 41.8% from $8.17 million a year earlier.
AvePoint (AVPT) Change in Accured Expenses (2019 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, AvePoint reported Change in Accured Expenses of $11.5 million, down 5.5% year-over-year; for FY2025, it came in at $3.38 million, down 84.9% from FY2024.
- Change in Accured Expenses carries a five-year compound annual growth rate of 28.4% (FY2020 to FY2025).
- Going back by year, Change in Accured Expenses was $22.44 million in FY2024 (+226.1%), $6.88 million in FY2023, -$2.55 million in FY2022 and $10.63 million in FY2021 (+994.3%).
- The five-year range for quarterly Change in Accured Expenses is -$27.9 million (Q1 2025) to $23.04 million (Q4 2024).
- Year-over-year, Change in Accured Expenses increased in four of the last six quarters, with growth averaging 80.0%.
- The fastest year-over-year change in Change in Accured Expenses over five years came in Q4 2023 (growth of 629.6%), and the weakest in Q4 2022 (a decline of 81.0%).
- Business Quant data shows AVPT's Change in Accured Expenses at -$23.2 million (Q1 2026), $19.55 million (Q4 2025) and $3.56 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 328.64 Bn | 313.71 Bn | 2.30 Bn | 369.00 Mn |
| 2 | CrowdStrike Holdings | 276.50 Bn | 256.94 Bn | 1.10 Bn | 72.38 Mn |
| 3 | Fortinet | 132.74 Bn | 118.67 Bn | 1.64 Bn | 75.10 Mn |
| 4 | Snowflake | 120.19 Bn | 107.50 Bn | 1.04 Bn | 108.85 Mn |
| 5 | Datadog | 99.51 Bn | 81.15 Bn | 881.34 Mn | 6.98 Mn |
| 6 | Okta | 35.35 Bn | 25.44 Bn | 641.00 Mn | -2.00 Mn |
| 7 | Axon Enterprise | 33.58 Bn | 28.10 Bn | 546.45 Mn | 256.58 Mn |
| 8 | Zscaler | 32.06 Bn | 18.18 Bn | - | - |
| 9 | MongoDB | 28.84 Bn | 19.30 Bn | 569.77 Mn | 43.09 Mn |
| 10 | AvePoint | 2.97 Bn | 1.16 Bn | 91.01 Mn | 11.58 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 11.58 Mn |
| Mar 31, 2026 | -23.20 Mn |
| Dec 31, 2025 | 19.55 Mn |
| Sep 30, 2025 | 3.56 Mn |
| Jun 30, 2025 | 8.17 Mn |
| Mar 31, 2025 | -27.90 Mn |
| Dec 31, 2024 | 23.04 Mn |
| Sep 30, 2024 | 8.86 Mn |
| Jun 30, 2024 | 4.84 Mn |
| Mar 31, 2024 | -14.29 Mn |
| Dec 31, 2023 | 12.21 Mn |
| Sep 30, 2023 | 1.95 Mn |
| Jun 30, 2023 | 5.55 Mn |
| Mar 31, 2023 | -12.83 Mn |
| Dec 31, 2022 | 1.67 Mn |
| Sep 30, 2022 | 2.43 Mn |
| Jun 30, 2022 | 5.07 Mn |
| Mar 31, 2022 | -11.73 Mn |
| Dec 31, 2021 | 8.79 Mn |
| Sep 30, 2021 | 5.98 Mn |
AvePoint 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=AVPT&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "AVPT", "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=AVPT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();