Magyar Bancorp (MGYR) Return on Sales [ROS] (2009 - 2026)
Magyar Bancorp (MGYR) recorded Return on Sales [ROS] of 102.06% in fiscal Q3 2026 (quarter ended Jun 30, 2026), down 6.93 percentage points from 108.99% a year earlier but up 1.09 percentage points from the prior quarter.
Magyar Bancorp (MGYR) Return on Sales [ROS] (2009 - 2026) Analysis & Trends
On a TTM basis, Magyar Bancorp's Return on Sales [ROS] came in at 103.70% as of Jun 30, 2026, down 8.14 percentage points year-over-year; for FY2025 (ended Sep 30, 2025), it was 106.23%, down 0.46 percentage points from FY2024.
- Annual Return on Sales [ROS] has a five-year change of +68.95 percentage points (FY2020 to FY2025).
- Across earlier fiscal years, Return on Sales [ROS] came in at 106.70% in FY2024 (+37.41 pp), 69.29% in FY2023 (+23.37 pp), 45.92% in FY2022 (+5.68 pp) and 40.23% in FY2021 (+2.95 pp).
- Quarterly Return on Sales [ROS] has ranged from 40.36% in fiscal Q4 2021 to 131.08% in fiscal Q4 2024 over the past five years.
- On a year-over-year basis, Return on Sales [ROS] rose in five of the last eight quarters, with an average year-over-year change of +5.24 percentage points.
- Peak year-over-year performance for Return on Sales [ROS] in the last five years was a gain of 48.57 percentage points in fiscal Q4 2024, against a drop of 24.57 percentage points in fiscal Q4 2025 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at 100.97% (Q2 2026), 105.62% (Q1 2026) and 106.51% (Q4 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 920.14 Bn | 897.90 Bn | - | 91.99% |
| 2 | Banco Santander Chile | 407.42 Bn | -7,109.68 Bn | - | - |
| 3 | Bank Of America | 353.20 Bn | -2,022.91 Bn | - | 89.38% |
| 4 | Hsbc Holdings | 307.88 Bn | -2,967.28 Bn | - | 48.15% |
| 5 | Morgan Stanley | 298.41 Bn | -244.41 Bn | - | 95.47% |
| 6 | Royal Bank Of Canada | 283.46 Bn | -546.54 Bn | - | 138.04% |
| 7 | Goldman Sachs | 264.45 Bn | -3,478.99 Bn | - | 139.33% |
| 8 | Mitsubishi Ufj Financial | 259.19 Bn | -159.36 Bn | 5.24 Bn | 70.61% |
| 9 | Wells Fargo & Company | 258.28 Bn | 93.72 Bn | - | 84.54% |
| 10 | Magyar Bancorp | 142.06 Mn | 142.06 Mn | - | - |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 102.06% |
| Mar 31, 2026 | 100.97% |
| Dec 31, 2025 | 105.62% |
| Sep 30, 2025 | 106.51% |
| Jun 30, 2025 | 108.99% |
| Mar 31, 2025 | 105.03% |
| Dec 31, 2024 | 104.39% |
| Sep 30, 2024 | 131.08% |
| Jun 30, 2024 | 107.54% |
| Mar 31, 2024 | 98.34% |
| Dec 31, 2023 | 84.87% |
| Sep 30, 2023 | 82.51% |
| Jun 30, 2023 | 74.46% |
| Mar 31, 2023 | 63.53% |
| Dec 31, 2022 | 56.09% |
| Sep 30, 2022 | 53.44% |
| Jun 30, 2022 | 46.01% |
| Mar 31, 2022 | 41.66% |
| Dec 31, 2021 | 41.43% |
| Sep 30, 2021 | 35.02% |
Magyar Bancorp Return on Sales [ROS] 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=return-on-sales-%5Bros%5D&ticker=MGYR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "ticker": "MGYR", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=return-on-sales-%5Bros%5D&ticker=MGYR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();