Good Times Restaurants (GTIM) Return on Assets [ROA] (2009 - 2026)
Good Times Restaurants' Return on Assets [ROA] came in at 2.88% for fiscal Q3 2026 (quarter ended Jun 30, 2026), up 1.28 percentage points from 1.59% a year earlier and up 0.54 percentage points from the prior quarter.
Good Times Restaurants (GTIM) Return on Assets [ROA] (2009 - 2026) Analysis & Trends
For FY2025 (ended Sep 30, 2025), Good Times Restaurants' Return on Assets [ROA] was 1.28%, down 0.82 percentage points from FY2024.
- Return on Assets [ROA] carries a four-year change of -17.75 percentage points (FY2021 to FY2025).
- Going back by fiscal year, Return on Assets [ROA] was 2.11% in FY2024 (-11.04 pp), 13.15% in FY2023 (+14.18 pp), -1.03% in FY2022 (-20.06 pp) and 19.03% in FY2021.
- The fiscal Q3 2026 figure represents the highest quarterly Return on Assets [ROA] since fiscal Q1 2024.
- Year-over-year, Return on Assets [ROA] increased in three of the last eight quarters, with an average year-over-year change of -2.50 percentage points.
- The fastest year-over-year change in Return on Assets [ROA] over five years came in fiscal Q1 2022 (a gain of 18.90 percentage points), and the weakest in fiscal Q1 2023 (a drop of 22.20 percentage points).
- Business Quant data shows GTIM's Return on Assets [ROA] at 2.33% (Q2 2026), 1.35% (Q1 2026) and 1.30% (Q4 2025) in the three fiscal quarters before Q3 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROA (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 199.07 Bn | 195.11 Bn | 6.47 Bn | 15.11% |
| 2 | Starbucks | 125.34 Bn | 125.35 Bn | - | 8.91% |
| 3 | Chipotle Mexican Grill | 45.50 Bn | 42.00 Bn | - | 15.04% |
| 4 | Yum Brands | 42.97 Bn | 38.79 Bn | 1.38 Bn | 39.46% |
| 5 | Restaurant Brands International | 27.07 Bn | 24.44 Bn | 1.40 Bn | 7.83% |
| 6 | Darden Restaurants | 25.87 Bn | 24.97 Bn | 1.68 Bn | 9.06% |
| 7 | Restaurant Brands International Limited Partnership | 24.67 Bn | 20.46 Bn | 1.40 Bn | 7.83% |
| 8 | Yum China Holdings | 16.86 Bn | 11.73 Bn | 652.00 Mn | 9.30% |
| 9 | Texas Roadhouse | 12.35 Bn | 11.68 Bn | 1.02 Bn | 11.63% |
| 10 | Good Times Restaurants | 13.30 Mn | 1.83 Mn | 147,000.00 | 4.78% |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 4.78% |
| Mar 31, 2026 | 1.56% |
| Dec 30, 2025 | 1.55% |
| Jul 1, 2025 | 2.88% |
| Apr 1, 2025 | 1.84% |
| Dec 31, 2024 | 2.00% |
| Jun 25, 2024 | 2.87% |
| Mar 26, 2024 | 13.31% |
| Dec 26, 2023 | 12.51% |
| Jun 27, 2023 | 13.78% |
| Mar 28, 2023 | 11.04% |
| Dec 27, 2022 | 0.27% |
| Jun 28, 2022 | 15.84% |
| Mar 29, 2022 | 16.33% |
| Dec 28, 2021 | 19.35% |
| Jun 29, 2021 | 4.19% |
| Mar 30, 2021 | -9.71% |
| Dec 29, 2020 | -26.13% |
| Sep 29, 2020 | -26.96% |
| Jun 30, 2020 | -18.82% |
Good Times Restaurants Return on Assets [ROA] 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-assets-%5Broa%5D&ticker=GTIM&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-assets-[roa]", "ticker": "GTIM", "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-assets-%5Broa%5D&ticker=GTIM&period=max&api_key=YOUR_API_KEY");
const data = await res.json();