Lam Research (LRCX) Accumulated Expenses (2009 - 2026)
Lam Research's Accumulated Expenses was $3.29 billion in fiscal Q1 2027 (quarter ended Sep 27, 2026), up 34.5% from $2.44 billion a year earlier and up 39.8% from the prior quarter.
Powered by 
Analysis
Lam Research (LRCX) Accumulated Expenses (2009 - 2026) Analysis & Trends
At the end of FY2026 (ended Jun 28, 2026), Accumulated Expenses at Lam Research came in at $2.35 billion, down 1.8% from FY2025.
- Accumulated Expenses shows a five-year compound annual growth rate of 6.5% (FY2021 to FY2026).
- In earlier fiscal years, Accumulated Expenses was $2.39 billion in FY2025 (+32.9%), $1.8 billion in FY2024 (-10.4%), $2.01 billion in FY2023 (+1.8%) and $1.97 billion in FY2022 (+14.8%).
- The fiscal Q1 2027 figure marks the highest quarterly Accumulated Expenses in data going back to fiscal Q4 2010.
- Compared with a year earlier, Accumulated Expenses was higher in seven of the last eight quarters, with growth averaging 13.3%.
- The best year-over-year quarter for Accumulated Expenses over five years was fiscal Q1 2027 (growth of 34.5%); the worst was fiscal Q4 2024 (a decline of 10.4%).
- Per Business Quant data, LRCX's Accumulated Expenses in the three fiscal quarters before Q1 2027 was $2.35 billion (Q4 2026), $2.08 billion (Q3 2026) and $2.27 billion (Q2 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | SK hynix | 24,576.54 Bn | 24,560.31 Bn | 43.99 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 10,284.32 Bn | 9,899.31 Bn | 27.22 Bn |
| 3 | Nvidia | 4,083.27 Bn | 3,857.43 Bn | 72.14 Bn |
| 4 | Broadcom | 1,582.22 Bn | 1,508.26 Bn | 20.46 Bn |
| 5 | Advanced Micro Devices | 844.94 Bn | 793.16 Bn | 7.21 Bn |
| 6 | Micron Technology | 820.10 Bn | 725.74 Bn | 47.05 Bn |
| 7 | Asml Holding | 581.15 Bn | 536.84 Bn | 5.90 Bn |
| 8 | Intel | 308.67 Bn | 171.38 Bn | 7.10 Bn |
| 9 | Applied Materials | 295.64 Bn | 261.08 Bn | 4.59 Bn |
| 10 | Lam Research | 277.80 Bn | 253.85 Bn | 4.30 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 27, 2026 | 3.29 Bn |
| Jun 28, 2026 | 2.35 Bn |
| Mar 29, 2026 | 2.08 Bn |
| Dec 28, 2025 | 2.27 Bn |
| Sep 28, 2025 | 2.44 Bn |
| Jun 29, 2025 | 2.39 Bn |
| Mar 30, 2025 | 2.00 Bn |
| Dec 29, 2024 | 2.10 Bn |
| Sep 29, 2024 | 2.20 Bn |
| Jun 30, 2024 | 1.80 Bn |
| Mar 31, 2024 | 1.79 Bn |
| Dec 24, 2023 | 1.98 Bn |
| Sep 24, 2023 | 2.12 Bn |
| Jun 25, 2023 | 2.01 Bn |
| Mar 26, 2023 | 1.99 Bn |
| Dec 25, 2022 | 2.07 Bn |
| Sep 25, 2022 | 1.95 Bn |
| Jun 26, 2022 | 1.97 Bn |
| Mar 27, 2022 | 1.63 Bn |
| Dec 26, 2021 | 1.77 Bn |
API Access
Lam Research 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=LRCX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "LRCX", "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=LRCX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();