Realloys (ALOY) Return on Sales [ROS] (2016 - 2026)
Realloys (ALOY) reported Return on Sales [ROS] of -4577.61% for Q2 2026, down 4372.61 percentage points from -205.00% a year earlier but up 7838.39 percentage points from the prior quarter.
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Analysis
Realloys (ALOY) Return on Sales [ROS] (2016 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Realloys' Return on Sales [ROS] came in at -4460.02%, down 4257.42 percentage points year-over-year; for FY2025, it was -165.69%, down 36.78 percentage points from FY2024.
- Return on Sales [ROS] has a five-year change of -153.43 percentage points (FY2020 to FY2025).
- By year, Return on Sales [ROS] came in at -128.91% in FY2024 (+41.65 pp), -170.56% in FY2023 (-78.89 pp), -91.67% in FY2022 (-54.40 pp) and -37.27% in FY2021 (-25.01 pp).
- Five-year quarterly Return on Sales [ROS] spans a low of -12416.01% in Q1 2026 and a high of -33.90% in Q3 2021.
- Year over year, Return on Sales [ROS] has now declined in each of the last three quarters, with an average year-over-year change of -2099.66 percentage points over the last eight quarters.
- The high point for year-over-year Return on Sales [ROS] in five years was Q1 2024 (a gain of 93.60 percentage points); the low point was Q1 2026 (a drop of 12243.28 percentage points).
- Per Business Quant data, the three quarters before Q2 2026 came in at -12416.01% (Q1 2026), -247.99% (Q4 2025) and -62.97% (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | Palantir Technologies | 453.55 Bn | 422.61 Bn | 1.64 Bn | 47.12% |
| 2 | Oracle | 430.98 Bn | 303.54 Bn | - | 34.78% |
| 3 | Sap Se | 256.23 Bn | 177.31 Bn | 8.40 Bn | 26.76% |
| 4 | Salesforce | 193.08 Bn | 148.96 Bn | 8.70 Bn | 20.55% |
| 5 | ServiceNow | 138.94 Bn | 117.40 Bn | 2.82 Bn | 4.06% |
| 6 | Automatic Data Processing | 102.51 Bn | 84.63 Bn | 2.51 Bn | -33.33% |
| 7 | Intuit | 75.44 Bn | 54.79 Bn | 3.44 Bn | 10.91% |
| 8 | Relx | 60.79 Bn | 57.76 Bn | - | - |
| 9 | Strategy | 56.32 Bn | 49.31 Bn | 81.55 Mn | -6,808.11% |
| 10 | Realloys | 558.51 Mn | 558.51 Mn | 475,000.00 | -4,577.61% |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | -4,577.61% |
| Mar 31, 2026 | -12,416.01% |
| Dec 31, 2025 | -247.99% |
| Sep 30, 2025 | -62.97% |
| Jun 30, 2025 | -205.00% |
| Mar 31, 2025 | -172.73% |
| Dec 31, 2024 | -151.46% |
| Sep 30, 2024 | -113.32% |
| Jun 30, 2024 | -120.48% |
| Mar 31, 2024 | -133.04% |
| Dec 31, 2023 | -134.46% |
| Sep 30, 2023 | -119.32% |
| Jun 30, 2023 | -194.10% |
| Mar 31, 2023 | -226.64% |
| Dec 31, 2022 | -108.39% |
| Sep 30, 2022 | -98.29% |
| Jun 30, 2022 | -83.52% |
| Mar 31, 2022 | -80.25% |
| Dec 31, 2021 | -107.07% |
| Sep 30, 2021 | -33.90% |
API Access
Realloys 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=ALOY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "ticker": "ALOY", "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=ALOY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();