Globalfoundries (GFS) Price to Earnings (2022 - 2026)
Globalfoundries' Price to Earnings came in at 63.98 for Q2 2026, up 101.3% from the prior quarter.
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Analysis
Globalfoundries (GFS) Price to Earnings (2022 - 2026) Analysis & Trends
For FY2025, Globalfoundries' Price to Earnings was 21.93.
- Going back by year, Price to Earnings was 32.89 in FY2023 (+61.3%) and 20.39 in FY2022.
- The Q2 2026 figure represents the highest quarterly Price to Earnings since Q1 2022.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Nvidia | 5,757.49 Bn | 5,531.65 Bn | 72.14 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,520.17 Bn | 2,145.88 Bn | 27.22 Bn |
| 3 | Broadcom | 1,730.62 Bn | 1,656.67 Bn | 20.46 Bn |
| 4 | Micron Technology | 1,201.21 Bn | 1,139.97 Bn | 35.06 Bn |
| 5 | Advanced Micro Devices | 1,031.02 Bn | 987.76 Bn | 6.20 Bn |
| 6 | Asml Holding | 716.82 Bn | 672.66 Bn | 5.90 Bn |
| 7 | Intel | 585.95 Bn | 470.68 Bn | 6.51 Bn |
| 8 | Lam Research | 432.69 Bn | 409.49 Bn | 3.48 Bn |
| 9 | Applied Materials | 430.35 Bn | 395.79 Bn | 4.59 Bn |
| 10 | Globalfoundries | 26.99 Bn | 15.36 Bn | 505.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 63.98 |
| Mar 31, 2026 | 31.78 |
| Dec 31, 2025 | 21.93 |
| Sep 30, 2024 | 27.14 |
| Jun 30, 2024 | 31.34 |
| Mar 31, 2024 | 29.49 |
| Dec 31, 2023 | 30.52 |
| Sep 30, 2023 | 20.66 |
| Jun 30, 2023 | 21.69 |
| Mar 31, 2023 | 23.89 |
| Dec 31, 2022 | 18.68 |
| Sep 30, 2022 | 29.29 |
| Jun 30, 2022 | 39.22 |
| Mar 31, 2022 | 301.80 |
API Access
Globalfoundries Price to Earnings 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=price-to-earnings&ticker=GFS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "price-to-earnings", "ticker": "GFS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=price-to-earnings&ticker=GFS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();