Landmark Bancorp (LARK) Change in Accured Expenses (2012 - 2026)
Landmark Bancorp's Change in Accured Expenses came in at $7.01 million for Q1 2026, up 151.5% from $2.79 million a year earlier and up 512.6% from the prior quarter.
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Analysis
Landmark Bancorp (LARK) Change in Accured Expenses (2012 - 2026) Analysis & Trends
Over the trailing twelve months to Mar 31, 2026, Landmark Bancorp reported Change in Accured Expenses of $7 million, up 136.5% year-over-year; for FY2025, it came in at $2.78 million, up 118.0% from FY2024.
- Change in Accured Expenses carries a five-year compound annual growth rate of -6.2% (FY2020 to FY2025).
- Going back by year, Change in Accured Expenses was $1.28 million in FY2024, -$3.37 million in FY2023, $5 million in FY2022 and -$736,000 in FY2021.
- The Q1 2026 figure represents the highest quarterly Change in Accured Expenses since Q1 2013.
- Year-over-year, Change in Accured Expenses increased in three of the last four quarters, with growth averaging 97.4%.
- The fastest year-over-year change in Change in Accured Expenses over five years came in Q1 2025 (growth of 153.0%), and the weakest in Q2 2023 (a decline of 93.2%).
- Business Quant data shows LARK's Change in Accured Expenses at $1.14 million (Q4 2025), $2.29 million (Q3 2025) and -$3.44 million (Q2 2025) in the three quarters before Q1 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 883.45 Bn | 912.92 Bn | - | 22.64 Bn |
| 2 | Banco Santander Chile | 401.52 Bn | 521.46 Bn | - | - |
| 3 | Bank Of America | 377.43 Bn | -1,998.64 Bn | - | 27.13 Bn |
| 4 | Hsbc Holdings | 330.31 Bn | 330.37 Bn | - | - |
| 5 | Morgan Stanley | 299.17 Bn | -209.92 Bn | - | - |
| 6 | Royal Bank Of Canada | 274.08 Bn | 124.82 Bn | - | - |
| 7 | Mitsubishi Ufj Financial | 270.70 Bn | -1,317.79 Bn | 10.89 Bn | - |
| 8 | Goldman Sachs | 263.13 Bn | -3,293.98 Bn | - | - |
| 9 | Wells Fargo & Company | 243.64 Bn | 245.78 Bn | - | 14.35 Bn |
| 10 | Landmark Bancorp | 191.31 Mn | 191.31 Mn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 7.01 Mn |
| Dec 31, 2025 | 1.14 Mn |
| Sep 30, 2025 | 2.29 Mn |
| Jun 30, 2025 | -3.44 Mn |
| Mar 31, 2025 | 2.79 Mn |
| Dec 31, 2024 | -4.57 Mn |
| Sep 30, 2024 | 5.09 Mn |
| Jun 30, 2024 | -339,000.00 |
| Mar 31, 2024 | 1.10 Mn |
| Dec 31, 2023 | -3.82 Mn |
| Sep 30, 2023 | 2.12 Mn |
| Jun 30, 2023 | 22,000.00 |
| Mar 31, 2023 | -1.70 Mn |
| Dec 31, 2022 | 2.76 Mn |
| Sep 30, 2022 | 3.01 Mn |
| Jun 30, 2022 | 322,000.00 |
| Mar 31, 2022 | -1.09 Mn |
| Dec 31, 2021 | -3.30 Mn |
| Sep 30, 2021 | 4.60 Mn |
| Jun 30, 2021 | -1.55 Mn |
API Access
Landmark Bancorp Change in Accured 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=change-in-accured-expenses&ticker=LARK&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "LARK", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=LARK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();