Lakeland Financial (LKFN) Operating Expenses (2010 - 2026)
Lakeland Financial (LKFN) reported Operating Expenses of $34.46 million for Q2 2026, up 13.2% from $30.43 million a year earlier but down 2.0% from the prior quarter.
Lakeland Financial (LKFN) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Lakeland Financial's Operating Expenses came in at $138.02 million, up 11.1% year-over-year; for FY2025, it was $131.61 million, up 5.2% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 7.6% (FY2020 to FY2025).
- By year, Operating Expenses came in at $125.08 million in FY2024 (-4.3%), $130.71 million in FY2023 (+18.6%), $110.21 million in FY2022 (+5.7%) and $104.29 million in FY2021 (+14.3%).
- Five-year quarterly Operating Expenses spans a low of $24.93 million in Q4 2021 and a high of $42.73 million in Q2 2023.
- Year over year, Operating Expenses has now increased in each of the last four quarters, with growth averaging 6.4% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q2 2023 (growth of 53.1%); the low point was Q2 2024 (a decline of 22.0%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $35.15 million (Q1 2026), $33.45 million (Q4 2025) and $34.97 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 879.54 Bn | 909.01 Bn | - | 27.32 Bn |
| 2 | Banco Santander Chile | 418.48 Bn | 538.42 Bn | - | -3,218.32 Bn |
| 3 | Bank Of America | 381.92 Bn | -1,994.15 Bn | - | 18.63 Bn |
| 4 | Hsbc Holdings | 341.10 Bn | 341.15 Bn | - | - |
| 5 | Morgan Stanley | 295.71 Bn | -213.38 Bn | - | 13.90 Bn |
| 6 | Royal Bank Of Canada | 275.38 Bn | 126.13 Bn | - | 7.02 Bn |
| 7 | Mitsubishi Ufj Financial | 273.79 Bn | -1,314.70 Bn | 10.89 Bn | 17.50 Bn |
| 8 | Goldman Sachs | 262.78 Bn | -3,294.34 Bn | - | 11.67 Bn |
| 9 | Wells Fargo & Company | 242.61 Bn | 244.75 Bn | - | 13.66 Bn |
| 10 | Lakeland Financial | 1.43 Bn | 753.85 Mn | - | 34.46 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 34.46 Mn |
| Mar 31, 2026 | 35.15 Mn |
| Dec 31, 2025 | 33.45 Mn |
| Sep 30, 2025 | 34.97 Mn |
| Jun 30, 2025 | 30.43 Mn |
| Mar 31, 2025 | 32.76 Mn |
| Dec 31, 2024 | 30.65 Mn |
| Sep 30, 2024 | 30.39 Mn |
| Jun 30, 2024 | 33.33 Mn |
| Mar 31, 2024 | 30.71 Mn |
| Dec 31, 2023 | 29.45 Mn |
| Sep 30, 2023 | 29.10 Mn |
| Jun 30, 2023 | 42.73 Mn |
| Mar 31, 2023 | 29.43 Mn |
| Dec 31, 2022 | 27.43 Mn |
| Sep 30, 2022 | 27.89 Mn |
| Jun 30, 2022 | 27.91 Mn |
| Mar 31, 2022 | 26.97 Mn |
| Dec 31, 2021 | 24.93 Mn |
| Sep 30, 2021 | 25.97 Mn |
Lakeland Financial 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=LKFN&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "LKFN", "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=LKFN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();