MBody AI (MBAI) EV to EBITDA (2019 - 2023)
MBody AI's (MBAI) EV to EBITDA came in at -0.38 for the quarter ended Sep 30, 2023, compared with -4.66 a year earlier.
MBody AI (MBAI) EV to EBITDA (2019 - 2023) Analysis & Trends
For the trailing twelve months through Sep 30, 2023, EV to EBITDA at MBody AI was -17.44; for the year ended Dec 31, 2022, it was -1.91.
- In prior years, MBody AI's EV to EBITDA was 0.59 in the year ended Dec 31, 2021, -0.30 in the year ended Dec 31, 2020 and 0.19 in the year ended Dec 31, 2019.
- The figure for the quarter ended Sep 30, 2023 stands as the highest quarterly EV to EBITDA since the quarter ended Dec 31, 2021.
- According to Business Quant data, EV to EBITDA for the three prior quarters was -2.27 (quarter ended Jun 30, 2023), -5.47 (quarter ended Mar 31, 2023) and -8.25 (quarter ended Dec 31, 2022).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn |
| 10 | MBody AI | 3.79 Mn | 3.57 Mn | - |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2023 | -0.38 |
| Jun 30, 2023 | -2.27 |
| Mar 31, 2023 | -5.47 |
| Dec 31, 2022 | -8.25 |
| Sep 30, 2022 | -4.66 |
| Jun 30, 2022 | -5.25 |
| Mar 31, 2022 | -1.69 |
| Dec 31, 2021 | 0.77 |
| Sep 30, 2021 | -1.72 |
| Jun 30, 2021 | 0.56 |
| Mar 31, 2021 | 1.22 |
| Dec 31, 2020 | -0.27 |
| Sep 30, 2020 | -1.43 |
| Jun 30, 2020 | -0.82 |
| Mar 31, 2020 | -0.04 |
| Dec 31, 2019 | 0.19 |
MBody AI EV to 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=ev-to-ebitda&ticker=MBAI&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "ev-to-ebitda", "ticker": "MBAI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=ev-to-ebitda&ticker=MBAI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();