Harrow (HROW) Operating Expenses (2010 - 2026)
Harrow (HROW) reported Operating Expenses of $61.37 million for Q2 2026, up 70.0% from $36.1 million a year earlier and up 24.9% from the prior quarter.
Harrow (HROW) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Harrow's Operating Expenses came in at $204.71 million, up 31.8% year-over-year; for FY2025, it was $173.85 million, up 22.8% from FY2024.
- Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 38.6% (FY2020 to FY2025).
- By year, Operating Expenses came in at $141.55 million in FY2024 (+57.1%), $90.12 million in FY2023 (+47.0%), $61.29 million in FY2022 (+16.4%) and $52.65 million in FY2021 (+54.7%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q2 2010.
- Year over year, Operating Expenses has now increased in each of the last 14 quarters, with growth averaging 33.4% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q4 2021 (growth of 109.9%); the low point was Q3 2022 (a decline of 7.4%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $49.13 million (Q1 2026), $55.03 million (Q4 2025) and $39.18 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Eli Lilly | 1,136.92 Bn | 1,105.50 Bn | 19.71 Bn | 7.95 Bn |
| 2 | Johnson & Johnson | 617.57 Bn | 536.10 Bn | 17.26 Bn | 10.12 Bn |
| 3 | AbbVie | 501.37 Bn | 474.48 Bn | 12.70 Bn | 10.56 Bn |
| 4 | Merck | 367.96 Bn | 322.33 Bn | 12.21 Bn | 12.80 Bn |
| 5 | Novartis Ag | 297.98 Bn | 261.97 Bn | 11.24 Bn | 6.09 Bn |
| 6 | Astrazeneca | 261.45 Bn | 235.14 Bn | 12.86 Bn | 9.70 Bn |
| 7 | Amgen | 236.30 Bn | 191.70 Bn | 7.24 Bn | 6.54 Bn |
| 8 | Gilead Sciences | 190.27 Bn | 164.46 Bn | 6.22 Bn | 18.20 Bn |
| 9 | Pfizer | 169.65 Bn | 116.29 Bn | 10.94 Bn | 6.68 Bn |
| 10 | Harrow | 1.06 Bn | 733.88 Mn | 50.36 Mn | 61.37 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 61.37 Mn |
| Mar 31, 2026 | 49.13 Mn |
| Dec 31, 2025 | 55.03 Mn |
| Sep 30, 2025 | 39.18 Mn |
| Jun 30, 2025 | 36.10 Mn |
| Mar 31, 2025 | 43.54 Mn |
| Dec 31, 2024 | 39.80 Mn |
| Sep 30, 2024 | 35.92 Mn |
| Jun 30, 2024 | 34.87 Mn |
| Mar 31, 2024 | 30.96 Mn |
| Dec 31, 2023 | 29.93 Mn |
| Sep 30, 2023 | 22.45 Mn |
| Jun 30, 2023 | 21.12 Mn |
| Mar 31, 2023 | 16.62 Mn |
| Dec 31, 2022 | 15.94 Mn |
| Sep 30, 2022 | 16.20 Mn |
| Jun 30, 2022 | 15.10 Mn |
| Mar 31, 2022 | 14.06 Mn |
| Dec 31, 2021 | 16.86 Mn |
| Sep 30, 2021 | 17.48 Mn |
Harrow 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=HROW&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "HROW", "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=HROW&period=max&api_key=YOUR_API_KEY");
const data = await res.json();