Kun Peng International (KPEA) Operating Expenses (2010 - 2026)
Kun Peng International (KPEA) reported Operating Expenses of $288,809 for fiscal Q3 2026 (quarter ended Jun 30, 2026), down 37.2% from $459,529 a year earlier and down 12.4% from the prior quarter.
Kun Peng International (KPEA) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Kun Peng International's Operating Expenses came in at $1.52 million, down 51.7% year-over-year; for FY2025 (ended Sep 30, 2025), it was $2.74 million, down 21.2% from FY2024.
- Operating Expenses has declined for three consecutive fiscal years, though with a five-year compound annual growth rate of 24.8% (FY2020 to FY2025).
- By fiscal year, Operating Expenses came in at $3.47 million in FY2024 (-39.4%), $5.73 million in FY2023 (-31.1%), $8.32 million in FY2022 (+30.8%) and $6.36 million in FY2021 (+602.7%).
- The fiscal Q3 2026 figure ranks as the lowest quarterly Operating Expenses since fiscal Q3 2020.
- Year over year, Operating Expenses has now declined in each of the last six quarters, with an average decline of 37.1% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was fiscal Q1 2022 (growth of 343.2%); the low point was fiscal Q1 2023 (a decline of 69.7%).
- Per Business Quant data, the three fiscal quarters before Q3 2026 came in at $329,856 (Q2 2026), $357,874 (Q1 2026) and $547,813 (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Amazon Com | 2,654.24 Bn | 2,170.93 Bn | 104.83 Bn | 173.15 Bn |
| 2 | Home Depot | 289.19 Bn | 282.43 Bn | 16.12 Bn | 9.28 Bn |
| 3 | Tjx Companies | 143.39 Bn | 120.94 Bn | 5.07 Bn | 3.09 Bn |
| 4 | Lowes Companies | 105.29 Bn | 98.25 Bn | 8.58 Bn | 4.46 Bn |
| 5 | Ross Stores | 75.79 Bn | 58.72 Bn | 2.12 Bn | 1.02 Bn |
| 6 | Target | 72.00 Bn | 66.59 Bn | 8.94 Bn | 5.73 Bn |
| 7 | O Reilly Automotive | 70.61 Bn | 69.69 Bn | 2.52 Bn | 1.53 Bn |
| 8 | Carvana | 66.52 Bn | 58.12 Bn | 1.38 Bn | 704.00 Mn |
| 9 | Autozone | 47.57 Bn | 46.47 Bn | 2.52 Bn | 1.60 Bn |
| 10 | Kun Peng International | 9.60 Mn | 9.52 Mn | -5,644.00 | 288,809.00 |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 288,809.00 |
| Mar 31, 2026 | 329,856.00 |
| Dec 31, 2025 | 357,874.00 |
| Sep 30, 2025 | 547,813.00 |
| Jun 30, 2025 | 459,529.00 |
| Mar 31, 2025 | 825,522.00 |
| Dec 31, 2024 | 998,922.00 |
| Sep 30, 2024 | 869,130.00 |
| Jun 30, 2024 | 776,281.00 |
| Mar 31, 2024 | 848,683.00 |
| Dec 31, 2023 | 979,807.00 |
| Sep 30, 2023 | 2.03 Mn |
| Jun 30, 2023 | 1.65 Mn |
| Mar 31, 2023 | 947,904.00 |
| Dec 31, 2022 | 1.10 Mn |
| Sep 30, 2022 | 1.14 Mn |
| Jun 30, 2022 | 1.23 Mn |
| Mar 31, 2022 | 2.30 Mn |
| Dec 31, 2021 | 3.65 Mn |
| Sep 30, 2021 | 1.98 Mn |
Kun Peng International 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=KPEA&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "KPEA", "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=KPEA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();