MicroAlgo (MLGO) Operating Expenses (2020 - 2023)
MicroAlgo (MLGO) recorded Operating Expenses of -$12.3 million in FY2025, compared with -$18.59 million in FY2024.
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Analysis
MicroAlgo (MLGO) Operating Expenses (2020 - 2023) Analysis & Trends
Starting with FY2020, MicroAlgo's Operating Expenses history includes 6 years.
- The FY2025 figure is the highest annual Operating Expenses since FY2022.
- Per Business Quant data, MLGO's Operating Expenses was -$18.59 million in FY2024, -$59.18 million in FY2023, $26.81 million in FY2022 (+7.0%) and $25.07 million in FY2021.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,515.53 Bn | 5,289.69 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,348.76 Bn | 1,974.46 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,668.85 Bn | 1,594.89 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,189.94 Bn | 1,128.71 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 992.04 Bn | 948.79 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 682.73 Bn | 638.57 Bn | 5.90 Bn | - |
| 7 | Intel | 585.14 Bn | 469.87 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 393.49 Bn | 370.29 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 386.29 Bn | 351.73 Bn | 4.59 Bn | 1.51 Bn |
| 10 | MicroAlgo | 72.94 Mn | -1.04 Bn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2023 | -20.97 Mn |
| Mar 31, 2023 | 6.37 Mn |
| Dec 31, 2022 | 26.45 Mn |
| Sep 30, 2022 | 100,064.00 |
| Jun 30, 2022 | 90,520.00 |
| Mar 31, 2022 | 4.22 Mn |
| Dec 31, 2021 | 24.44 Mn |
| Sep 30, 2021 | 334,341.00 |
| Jun 30, 2021 | 218,846.00 |
| Mar 31, 2021 | 78,705.00 |
| Dec 31, 2020 | 70,143.00 |
API Access
MicroAlgo Operating 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=operating-expenses&ticker=MLGO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MLGO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=MLGO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();