O Reilly Automotive (ORLY) Accumulated Expenses (2010 - 2026)
O Reilly Automotive (ORLY) posted Accumulated Expenses of $214.31 million for Q2 2026, up 34.9% from $158.84 million a year earlier but down 33.4% from the prior quarter.
Powered by 
Analysis
O Reilly Automotive (ORLY) Accumulated Expenses (2010 - 2026) Analysis & Trends
At the end of FY2025, O Reilly Automotive's Accumulated Expenses came in at $297.3 million, up 99.0% from FY2024.
- Annual Accumulated Expenses shows a five-year compound annual growth rate of 22.2% (FY2020 to FY2025).
- In prior years, O Reilly Automotive's Accumulated Expenses was $149.4 million in FY2024 (+16.2%), $128.55 million in FY2023 (-7.5%), $138.93 million in FY2022 (+7.9%) and $128.8 million in FY2021 (+17.9%).
- Quarterly Accumulated Expenses has run from a low of $122.55 million in Q3 2021 to a high of $321.9 million in Q1 2026 over five years.
- On a year-over-year basis, Accumulated Expenses has increased in each of the last seven quarters, with growth averaging 43.1% over the last eight quarters.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was Q1 2026, with growth of 109.0%; the weakest was Q3 2023, with a decline of 9.5%.
- According to Business Quant data, Accumulated Expenses for the three prior quarters was $321.9 million (Q1 2026), $297.3 million (Q4 2025) and $180.14 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Amazon Com | 2,686.58 Bn | 2,203.28 Bn | 104.83 Bn |
| 2 | Home Depot | 283.87 Bn | 277.11 Bn | 16.12 Bn |
| 3 | Tjx Companies | 145.82 Bn | 123.36 Bn | 5.07 Bn |
| 4 | Lowes Companies | 103.43 Bn | 96.39 Bn | 8.58 Bn |
| 5 | Ross Stores | 74.61 Bn | 57.54 Bn | 2.12 Bn |
| 6 | Target | 71.16 Bn | 65.74 Bn | 8.94 Bn |
| 7 | O Reilly Automotive | 69.71 Bn | 68.80 Bn | 2.52 Bn |
| 8 | Carvana | 68.78 Bn | 60.38 Bn | 1.38 Bn |
| 9 | Autozone | 46.23 Bn | 45.13 Bn | 2.52 Bn |
| 10 | JD.com | 32.27 Bn | -77.91 Bn | 8.71 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 214.31 Mn |
| Mar 31, 2026 | 321.90 Mn |
| Dec 31, 2025 | 297.30 Mn |
| Sep 30, 2025 | 180.14 Mn |
| Jun 30, 2025 | 158.84 Mn |
| Mar 31, 2025 | 154.01 Mn |
| Dec 31, 2024 | 149.40 Mn |
| Sep 30, 2024 | 123.51 Mn |
| Jun 30, 2024 | 125.86 Mn |
| Mar 31, 2024 | 130.97 Mn |
| Dec 31, 2023 | 128.55 Mn |
| Sep 30, 2023 | 128.89 Mn |
| Jun 30, 2023 | 131.78 Mn |
| Mar 31, 2023 | 136.72 Mn |
| Dec 31, 2022 | 138.93 Mn |
| Sep 30, 2022 | 142.39 Mn |
| Jun 30, 2022 | 137.28 Mn |
| Mar 31, 2022 | 137.63 Mn |
| Dec 31, 2021 | 128.80 Mn |
| Sep 30, 2021 | 122.55 Mn |
API Access
O Reilly Automotive Accumulated 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=accumulated-expenses&ticker=ORLY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "ORLY", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=ORLY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();