Monolithic Power Systems (MPWR) Accumulated Expenses (2009 - 2026)
Monolithic Power Systems (MPWR) recorded Accumulated Expenses of $267.66 million in Q3 2026, up 32.8% from $201.51 million a year earlier and up 20.5% from the prior quarter.
Monolithic Power Systems (MPWR) Accumulated Expenses (2009 - 2026) Analysis & Trends
At the end of FY2025, Monolithic Power Systems reported Accumulated Expenses of $145.13 million, up 13.3% from FY2024.
- Annual Accumulated Expenses has increased for 11 straight years, with a five-year compound annual growth rate of 18.2% (FY2020 to FY2025).
- Across earlier years, Accumulated Expenses came in at $128.12 million in FY2024 (+10.7%), $115.79 million in FY2023 (+1.9%), $113.68 million in FY2022 (+39.9%) and $81.28 million in FY2021 (+29.1%).
- The Q3 2026 figure is the highest quarterly Accumulated Expenses in data going back to Q4 2014.
- On a year-over-year basis, Accumulated Expenses has increased for ten consecutive quarters, with growth averaging 26.4% over the last eight quarters.
- Peak year-over-year performance for Accumulated Expenses in the last five years was growth of 49.4% in Q1 2022, against a decline of 11.6% in Q1 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $222.08 million (Q2 2026), $213.69 million (Q1 2026) and $145.13 million (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | SK hynix | 24,980.27 Bn | 24,964.04 Bn | 43.99 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 10,337.48 Bn | 9,919.73 Bn | 31.04 Bn |
| 3 | Nvidia | 3,939.34 Bn | 3,713.50 Bn | 72.14 Bn |
| 4 | Broadcom | 1,653.85 Bn | 1,579.89 Bn | 20.46 Bn |
| 5 | Micron Technology | 880.27 Bn | 785.91 Bn | 47.05 Bn |
| 6 | Advanced Micro Devices | 875.86 Bn | 824.07 Bn | 7.21 Bn |
| 7 | Asml Holding | 576.26 Bn | 531.95 Bn | 5.90 Bn |
| 8 | Intel | 316.79 Bn | 179.50 Bn | 7.10 Bn |
| 9 | Applied Materials | 294.03 Bn | 294.03 Bn | 5.25 Bn |
| 10 | Monolithic Power Systems | 67.85 Bn | 62.72 Bn | 665.54 Mn |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 267.66 Mn |
| Jun 30, 2026 | 222.08 Mn |
| Mar 31, 2026 | 213.69 Mn |
| Dec 31, 2025 | 145.13 Mn |
| Sep 30, 2025 | 201.51 Mn |
| Jun 30, 2025 | 172.29 Mn |
| Mar 31, 2025 | 161.31 Mn |
| Dec 31, 2024 | 128.12 Mn |
| Sep 30, 2024 | 139.43 Mn |
| Jun 30, 2024 | 131.35 Mn |
| Mar 31, 2024 | 137.87 Mn |
| Dec 31, 2023 | 115.79 Mn |
| Sep 30, 2023 | 120.68 Mn |
| Jun 30, 2023 | 113.41 Mn |
| Mar 31, 2023 | 155.96 Mn |
| Dec 31, 2022 | 113.68 Mn |
| Sep 30, 2022 | 109.06 Mn |
| Jun 30, 2022 | 91.32 Mn |
| Mar 31, 2022 | 111.09 Mn |
| Dec 31, 2021 | 81.28 Mn |
Monolithic Power Systems 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=MPWR&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "MPWR", "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=MPWR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();