Synaptics (SYNA) Operating Expenses (2009 - 2026)
Synaptics (SYNA) reported Operating Expenses of $161.9 million for fiscal Q4 2026 (quarter ended Jun 27, 2026), up 11.1% from $145.7 million a year earlier and up 10.9% from the prior quarter.
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Analysis
Synaptics (SYNA) Operating Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended Jun 27, 2026), Synaptics posted Operating Expenses of $602.5 million, up 4.9% from FY2025.
- Operating Expenses has a five-year compound annual growth rate of 5.4% (FY2021 to FY2026).
- By fiscal year, Operating Expenses came in at $574.5 million in FY2025 (+6.1%), $541.4 million in FY2024 (-3.6%), $561.6 million in FY2023 (-5.2%) and $592.7 million in FY2022 (+27.7%).
- The fiscal Q4 2026 figure ranks as the highest quarterly Operating Expenses since fiscal Q3 2022.
- Year over year, Operating Expenses has now increased in each of the last three quarters, with growth averaging 5.6% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was fiscal Q2 2022 (growth of 60.5%); the low point was fiscal Q3 2023 (a decline of 16.7%).
- Per Business Quant data, the three fiscal quarters before Q4 2026 came in at $146 million (Q3 2026), $146.8 million (Q2 2026) and $147.8 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,638.19 Bn | 5,412.36 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,452.80 Bn | 2,078.51 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,695.44 Bn | 1,621.48 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,213.55 Bn | 1,152.31 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 1,034.54 Bn | 991.29 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 719.69 Bn | 675.53 Bn | 5.90 Bn | - |
| 7 | Intel | 601.78 Bn | 486.52 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 434.81 Bn | 411.60 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 428.57 Bn | 394.02 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Synaptics | 4.73 Bn | 2.99 Bn | 145.80 Mn | 161.90 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 161.90 Mn |
| Mar 28, 2026 | 146.00 Mn |
| Dec 27, 2025 | 146.80 Mn |
| Sep 27, 2025 | 147.80 Mn |
| Jun 28, 2025 | 145.70 Mn |
| Mar 29, 2025 | 142.10 Mn |
| Dec 28, 2024 | 137.40 Mn |
| Sep 28, 2024 | 149.30 Mn |
| Jun 29, 2024 | 144.50 Mn |
| Mar 30, 2024 | 127.70 Mn |
| Dec 30, 2023 | 126.90 Mn |
| Sep 30, 2023 | 142.30 Mn |
| Jun 24, 2023 | 139.20 Mn |
| Mar 25, 2023 | 138.10 Mn |
| Dec 24, 2022 | 140.60 Mn |
| Sep 24, 2022 | 143.70 Mn |
| Jun 25, 2022 | 142.00 Mn |
| Mar 26, 2022 | 165.70 Mn |
| Dec 25, 2021 | 147.50 Mn |
| Sep 25, 2021 | 137.50 Mn |
API Access
Synaptics 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=SYNA&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SYNA", "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=SYNA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();