Maison Solutions (MSS) Operating Expenses (2022 - 2026)
Maison Solutions' Operating Expenses was $10.36 million in fiscal Q3 2026 (quarter ended Jan 31, 2026), up 76.8% from $5.86 million a year earlier and up 32.7% from the prior quarter.
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Analysis
Maison Solutions (MSS) Operating Expenses (2022 - 2026) Analysis & Trends
On a trailing twelve-month basis, Maison Solutions' Operating Expenses was $33.27 million through Jan 31, 2026, up 41.4% year-over-year; for FY2025 (ended Apr 30, 2025), it came in at $27.61 million, up 92.7% from FY2024.
- Operating Expenses has now increased for three consecutive fiscal years, with a three-year compound annual growth rate of 44.7% (FY2022 to FY2025).
- In earlier fiscal years, Operating Expenses was $14.33 million in FY2024 (+15.8%), $12.37 million in FY2023 (+35.7%) and $9.11 million in FY2022.
- The fiscal Q3 2026 figure marks the highest quarterly Operating Expenses since fiscal Q1 2023.
- Compared with a year earlier, Operating Expenses has increased for seven straight quarters, with growth averaging 67.8% over the last seven quarters.
- The best year-over-year quarter for Operating Expenses over five years was fiscal Q2 2025 (growth of 142.5%); the worst was fiscal Q1 2024 (a decline of 77.0%).
- Per Business Quant data, MSS's Operating Expenses in the three fiscal quarters before Q3 2026 was $7.81 million (Q2 2026), $6.37 million (Q1 2026) and $8.72 million (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Walmart | 936.41 Bn | 892.84 Bn | 49.13 Bn | 39.75 Bn |
| 2 | Costco Wholesale | 440.73 Bn | 364.01 Bn | 12.19 Bn | 8.39 Bn |
| 3 | Sysco | 39.69 Bn | 33.94 Bn | 4.13 Bn | 3.15 Bn |
| 4 | Kroger | 38.21 Bn | 24.36 Bn | 7.86 Bn | 198.00 Mn |
| 5 | Dollar General | 29.54 Bn | 24.22 Bn | 3.68 Bn | 2.91 Bn |
| 6 | Caseys General Stores | 25.34 Bn | 23.33 Bn | 1.24 Bn | 754.11 Mn |
| 7 | Dollar Tree | 23.81 Bn | 20.43 Bn | 2.10 Bn | 1.43 Bn |
| 8 | US Foods Holding | 22.49 Bn | 22.29 Bn | 1.92 Bn | 1.48 Bn |
| 9 | Tractor Supply | 17.20 Bn | 16.37 Bn | 1.68 Bn | 1.02 Bn |
| 10 | Maison Solutions | 582,021.89 | -4.15 Mn | 7.54 Mn | 10.36 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jan 31, 2026 | 10.36 Mn |
| Oct 31, 2025 | 7.81 Mn |
| Jul 31, 2025 | 6.37 Mn |
| Apr 30, 2025 | 8.72 Mn |
| Jan 31, 2025 | 5.86 Mn |
| Oct 31, 2024 | 6.96 Mn |
| Jul 31, 2024 | 6.07 Mn |
| Apr 30, 2024 | 4.64 Mn |
| Jan 31, 2024 | 3.49 Mn |
| Oct 31, 2023 | 2.87 Mn |
| Jul 31, 2023 | 3.32 Mn |
| Apr 30, 2023 | -31.27 Mn |
| Jan 31, 2023 | 4.06 Mn |
| Oct 31, 2022 | 2.44 Mn |
| Jul 31, 2022 | 14.47 Mn |
API Access
Maison Solutions 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=MSS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MSS", "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=MSS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();