Digital Turbine (APPS) Accumulated Expenses (2014 - 2026)
Digital Turbine (APPS) recorded Accumulated Expenses of $87.22 million in fiscal Q4 2026 (quarter ended Mar 31, 2026), up 147.3% from $35.26 million a year earlier but down 7.3% from the prior quarter.
Digital Turbine (APPS) Accumulated Expenses (2014 - 2026) Analysis & Trends
Starting with fiscal Q4 2014, Digital Turbine's Accumulated Expenses history includes 48 quarters.
- Annual Accumulated Expenses has a five-year compound annual growth rate of 13.6% (FY2021 to FY2026).
- Across earlier fiscal years, Accumulated Expenses came in at $35.26 million in FY2025 (+3.9%), $33.93 million in FY2024 (-51.0%), $69.22 million in FY2023 (-27.3%) and $95.17 million in FY2022 (+106.0%).
- Quarterly Accumulated Expenses has ranged from $26.32 million in fiscal Q1 2025 to $111.17 million in fiscal Q3 2022 over the past five years.
- On a year-over-year basis, Accumulated Expenses has increased for five consecutive quarters, with growth averaging 70.0% over the last eight quarters.
- Peak year-over-year performance for Accumulated Expenses in the last five years was growth of 268.8% in fiscal Q2 2022, against a decline of 51.0% in fiscal Q4 2024 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $94.12 million (Q3 2026), $80.45 million (Q2 2026) and $79.89 million (Q1 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Alphabet | 4,180.92 Bn | 3,938.44 Bn | 73.85 Bn |
| 2 | Meta Platforms | 1,847.76 Bn | 1,550.28 Bn | 49.47 Bn |
| 3 | Netflix | 289.73 Bn | 249.92 Bn | 6.52 Bn |
| 4 | Alibaba Group Holding | 249.81 Bn | 67.68 Bn | 15.11 Bn |
| 5 | Shopify | 192.08 Bn | 169.26 Bn | 1.71 Bn |
| 6 | Uber Technologies | 139.76 Bn | 111.74 Bn | 6.38 Bn |
| 7 | Booking Holdings | 122.41 Bn | 55.46 Bn | - |
| 8 | PDD Holdings | 110.94 Bn | -140.99 Bn | 9.45 Bn |
| 9 | Spotify Technology | 100.32 Bn | 57.56 Bn | 1.86 Bn |
| 10 | Digital Turbine | 1.33 Bn | 1.18 Bn | - |
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 87.22 Mn |
| Dec 31, 2025 | 94.12 Mn |
| Sep 30, 2025 | 80.45 Mn |
| Jun 30, 2025 | 79.89 Mn |
| Mar 31, 2025 | 35.26 Mn |
| Dec 31, 2024 | 34.30 Mn |
| Sep 30, 2024 | 29.52 Mn |
| Jun 30, 2024 | 26.32 Mn |
| Mar 31, 2024 | 33.93 Mn |
| Dec 31, 2023 | 66.16 Mn |
| Sep 30, 2023 | 55.15 Mn |
| Jun 30, 2023 | 49.76 Mn |
| Mar 31, 2023 | 69.22 Mn |
| Dec 31, 2022 | 75.38 Mn |
| Sep 30, 2022 | 79.17 Mn |
| Jun 30, 2022 | 86.16 Mn |
| Mar 31, 2022 | 95.17 Mn |
| Dec 31, 2021 | 111.17 Mn |
| Sep 30, 2021 | 81.88 Mn |
| Jun 30, 2021 | 84.43 Mn |
Digital Turbine Accumulated 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=accumulated-expenses&ticker=APPS&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "APPS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=APPS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();