Jingbo Technology (SVMB) Operating Expenses (2015 - 2026)
Jingbo Technology's Operating Expenses came in at $687,207 for fiscal Q1 2027 (quarter ended May 31, 2026), up 33.4% from $515,071 a year earlier but down 84.3% from the prior quarter.
Jingbo Technology (SVMB) Operating Expenses (2015 - 2026) Analysis & Trends
Over the trailing twelve months to May 31, 2026, Jingbo Technology reported Operating Expenses of $6.64 million, up 43.3% year-over-year; for FY2026 (ended Feb 28, 2026), it came in at $6.47 million, up 27.1% from FY2025.
- Operating Expenses carries a five-year compound annual growth rate of 166.1% (FY2021 to FY2026).
- Going back by fiscal year, Operating Expenses was $5.09 million in FY2025 (+11.4%), $4.57 million in FY2024, $6.28 million in FY2023 (+5.7%) and $5.94 million in FY2022.
- The five-year range for quarterly Operating Expenses is $5,784 (fiscal Q2 2022) to $4.37 million (fiscal Q4 2026).
- Year-over-year, Operating Expenses has increased for three consecutive quarters, with growth averaging 13.4% over the last five quarters.
- The fastest year-over-year change in Operating Expenses over five years came in fiscal Q2 2024 (growth of 217.0%), and the weakest in fiscal Q1 2026 (a decline of 46.9%).
- Business Quant data shows SVMB's Operating Expenses at $4.37 million (Q4 2026), $744,672 (Q3 2026) and $836,388 (Q2 2026) in the three fiscal quarters before Q1 2027.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palantir Technologies | 450.50 Bn | 419.56 Bn | 1.64 Bn | 726.59 Mn |
| 2 | Oracle | 401.01 Bn | 273.57 Bn | - | 12.62 Bn |
| 3 | Sap Se | 257.03 Bn | 178.11 Bn | 8.40 Bn | -8.41 Bn |
| 4 | Salesforce | 187.04 Bn | 142.92 Bn | 8.70 Bn | 6.37 Bn |
| 5 | ServiceNow | 135.91 Bn | 114.37 Bn | 2.82 Bn | 2.66 Bn |
| 6 | Automatic Data Processing | 104.14 Bn | 86.27 Bn | 2.51 Bn | 4.34 Bn |
| 7 | Intuit | 72.30 Bn | 51.65 Bn | 3.44 Bn | 3.88 Bn |
| 8 | Relx | 60.17 Bn | 57.14 Bn | - | - |
| 9 | Strategy | 55.31 Bn | 48.30 Bn | 81.55 Mn | 8.41 Bn |
| 10 | Jingbo Technology | 555.32 Mn | 554.58 Mn | 89,520.00 | 687,207.00 |
Historic Data
| Date | Value |
|---|---|
| May 31, 2026 | 687,207.00 |
| Feb 28, 2026 | 4.37 Mn |
| Nov 30, 2025 | 744,672.00 |
| Aug 31, 2025 | 836,388.00 |
| May 31, 2025 | 515,071.00 |
| Feb 28, 2025 | 2.04 Mn |
| Nov 30, 2024 | 708,609.00 |
| Aug 31, 2024 | 1.37 Mn |
| May 31, 2024 | 970,894.00 |
| Nov 30, 2023 | 1.09 Mn |
| Aug 31, 2023 | 906,297.00 |
| May 31, 2023 | 1.46 Mn |
| Feb 28, 2023 | 573,444.00 |
| Nov 30, 2022 | 1.09 Mn |
| Aug 31, 2022 | 1.79 Mn |
| May 31, 2022 | 1.67 Mn |
| Feb 28, 2022 | 1.34 Mn |
| Nov 30, 2021 | 5,784.00 |
| Aug 31, 2021 | 19,938.00 |
| May 31, 2021 | 25,226.00 |
Jingbo Technology 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=SVMB&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SVMB", "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=SVMB&period=max&api_key=YOUR_API_KEY");
const data = await res.json();