OP Bancorp (OPBK) Operating Expenses (2017 - 2026)
OP Bancorp (OPBK) posted Operating Expenses of $14.83 million for Q2 2026, up 5.6% from $14.04 million a year earlier and up 4.2% from the prior quarter.
OP Bancorp (OPBK) Operating Expenses (2017 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at OP Bancorp was $56.98 million, up 6.1% year-over-year; for FY2025, it was $55.77 million, up 11.1% from FY2024.
- Annual Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 11.8% (FY2020 to FY2025).
- In prior years, OP Bancorp's Operating Expenses was $50.2 million in FY2024 (+5.2%), $47.73 million in FY2023 (+6.5%), $44.83 million in FY2022 (+25.0%) and $35.87 million in FY2021 (+12.3%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses in data going back to Q1 2017.
- On a year-over-year basis, Operating Expenses has increased in each of the last eight quarters, with growth averaging 9.2% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q2 2022, with growth of 30.9%; the weakest was Q3 2023, with a decline of 6.5%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $14.23 million (Q1 2026), $14.29 million (Q4 2025) and $13.63 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 890.60 Bn | 920.07 Bn | - | 27.32 Bn |
| 2 | Banco Santander Chile | 421.74 Bn | 541.68 Bn | - | -3,218.32 Bn |
| 3 | Bank Of America | 385.85 Bn | -1,990.22 Bn | - | 18.63 Bn |
| 4 | Hsbc Holdings | 345.94 Bn | 345.99 Bn | - | - |
| 5 | Morgan Stanley | 303.15 Bn | -205.95 Bn | - | 13.90 Bn |
| 6 | Royal Bank Of Canada | 279.82 Bn | 130.57 Bn | - | 7.02 Bn |
| 7 | Mitsubishi Ufj Financial | 274.62 Bn | -1,313.87 Bn | 10.89 Bn | 17.50 Bn |
| 8 | Goldman Sachs | 267.07 Bn | -3,290.04 Bn | - | 11.67 Bn |
| 9 | Wells Fargo & Company | 243.79 Bn | 245.93 Bn | - | 13.66 Bn |
| 10 | OP Bancorp | 226.74 Mn | -442.63 Mn | - | 14.83 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 14.83 Mn |
| Mar 31, 2026 | 14.23 Mn |
| Dec 31, 2025 | 14.29 Mn |
| Sep 30, 2025 | 13.63 Mn |
| Jun 30, 2025 | 14.04 Mn |
| Mar 31, 2025 | 13.81 Mn |
| Dec 31, 2024 | 13.13 Mn |
| Sep 30, 2024 | 12.72 Mn |
| Jun 30, 2024 | 12.19 Mn |
| Mar 31, 2024 | 12.16 Mn |
| Dec 31, 2023 | 11.98 Mn |
| Sep 30, 2023 | 11.54 Mn |
| Jun 30, 2023 | 12.30 Mn |
| Mar 31, 2023 | 11.91 Mn |
| Dec 31, 2022 | 11.33 Mn |
| Sep 30, 2022 | 12.34 Mn |
| Jun 30, 2022 | 11.50 Mn |
| Mar 31, 2022 | 9.66 Mn |
| Dec 31, 2021 | 9.59 Mn |
| Sep 30, 2021 | 9.52 Mn |
OP Bancorp 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=OPBK&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "OPBK", "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=OPBK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();