Haemonetics (HAE) EBITDA (2009 - 2026)
Haemonetics (HAE) recorded EBITDA of $84.57 million in fiscal Q1 2027 (quarter ended Jun 27, 2026), up 2.3% from $82.63 million a year earlier.
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Analysis
Haemonetics (HAE) EBITDA (2009 - 2026) Analysis & Trends
On a TTM basis, Haemonetics' EBITDA came in at $270.39 million as of Jun 27, 2026, down 23.0% year-over-year; for FY2026 (ended Mar 28, 2026), it was $268.45 million, down 20.4% from FY2025.
- Annual EBITDA has a five-year compound annual growth rate of 9.1% (FY2021 to FY2026).
- Across earlier fiscal years, EBITDA came in at $337.4 million in FY2025 (+28.7%), $262.1 million in FY2024 (+5.1%), $249.34 million in FY2023 (+39.7%) and $178.5 million in FY2022 (+2.6%).
- Quarterly EBITDA has ranged from $4.29 million in fiscal Q4 2026 to $99.54 million in fiscal Q4 2025 over the past five years.
- On a year-over-year basis, EBITDA rose in seven of the last eight quarters, with growth averaging 10.0%.
- Peak year-over-year performance for EBITDA in the last five years was growth of 101.4% in fiscal Q1 2023, against a decline of 95.7% in fiscal Q4 2026 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $4.29 million (Q4 2026), $94.78 million (Q3 2026) and $86.75 million (Q2 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | EBITDA (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 257.99 Bn | 237.02 Bn | 4.88 Bn | 2.90 Bn |
| 2 | Abbott Laboratories | 175.01 Bn | 145.44 Bn | 7.27 Bn | 2.75 Bn |
| 3 | Danaher | 164.93 Bn | 148.74 Bn | 3.61 Bn | 1.79 Bn |
| 4 | Intuitive Surgical | 151.84 Bn | 131.26 Bn | 1.96 Bn | 1.19 Bn |
| 5 | Medtronic | 119.05 Bn | 84.30 Bn | 6.34 Bn | 2.49 Bn |
| 6 | Stryker | 113.89 Bn | 100.00 Bn | 4.50 Bn | 1.96 Bn |
| 7 | Boston Scientific | 65.37 Bn | 60.13 Bn | 3.85 Bn | 1.53 Bn |
| 8 | Becton Dickinson | 53.36 Bn | 50.51 Bn | 2.32 Bn | 1.23 Bn |
| 9 | Edwards Lifesciences | 50.83 Bn | 34.83 Bn | 1.35 Bn | 555.90 Mn |
| 10 | Haemonetics | 5.31 Bn | 4.18 Bn | 202.80 Mn | 84.57 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 84.57 Mn |
| Mar 28, 2026 | 4.29 Mn |
| Dec 27, 2025 | 94.78 Mn |
| Sep 27, 2025 | 86.75 Mn |
| Jun 28, 2025 | 82.63 Mn |
| Mar 29, 2025 | 99.54 Mn |
| Dec 28, 2024 | 88.05 Mn |
| Sep 28, 2024 | 80.91 Mn |
| Jun 29, 2024 | 68.89 Mn |
| Mar 30, 2024 | 58.36 Mn |
| Dec 30, 2023 | 70.29 Mn |
| Sep 30, 2023 | 59.10 Mn |
| Jul 1, 2023 | 77.52 Mn |
| Apr 1, 2023 | 59.98 Mn |
| Dec 31, 2022 | 67.63 Mn |
| Oct 1, 2022 | 69.68 Mn |
| Jul 2, 2022 | 54.01 Mn |
| Apr 2, 2022 | 44.81 Mn |
| Jan 1, 2022 | 60.81 Mn |
| Oct 2, 2021 | 48.84 Mn |
API Access
Haemonetics EBITDA 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=ebitda&ticker=HAE&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ebitda", "ticker": "HAE", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=ebitda&ticker=HAE&period=max&api_key=YOUR_API_KEY");
const data = await res.json();