Hyperscale Data (GPUS) Amortizatization of Intangibles (2011 - 2022)
Hyperscale Data's Amortizatization of Intangibles was $80,000 in Q1 2022, down 23.1% from $104,000 a year earlier but up 1.3% from the prior quarter.
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Analysis
Hyperscale Data (GPUS) Amortizatization of Intangibles (2011 - 2022) Analysis & Trends
On a trailing twelve-month basis, Hyperscale Data's Amortizatization of Intangibles was $351,000 through Mar 31, 2022, down 1.6% year-over-year; for FY2021, it was $375,000, up 11.6% from FY2020.
- Amortizatization of Intangibles shows a four-year compound annual growth rate of 57.9% (FY2017 to FY2021).
- In earlier years, Amortizatization of Intangibles was $336,000 in FY2020 (-33.2%), $502,656 in FY2019 (+9.4%), $459,656 in FY2018 (+661.8%) and $60,335 in FY2017.
- Quarterly Amortizatization of Intangibles has moved between $4,000 (Q3 2017) and $359,582 (Q4 2018) over five years.
- Compared with a year earlier, Amortizatization of Intangibles was higher in three of the last eight quarters, with an average decline of 6.2%.
- The best year-over-year quarter for Amortizatization of Intangibles over five years was Q3 2018 (growth of 734.0%); the worst was Q4 2019 (a decline of 71.6%).
- Per Business Quant data, GPUS's Amortizatization of Intangibles in the three quarters before Q1 2022 was $79,000 (Q4 2021), $105,000 (Q3 2021) and $87,000 (Q2 2021).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Amort. of Intangibles (Qtr) |
|---|---|---|---|---|---|
| 1 | Coinbase Global | 48.27 Bn | 5.08 Bn | - | - |
| 2 | PayPal Holdings | 45.51 Bn | 3.74 Bn | - | - |
| 3 | Block | 44.67 Bn | 15.93 Bn | 3.17 Bn | - |
| 4 | Iren | 31.78 Bn | 19.37 Bn | 103.89 Mn | - |
| 5 | Corpay | 25.91 Bn | 16.11 Bn | - | 81.60 Mn |
| 6 | Fiserv | 23.59 Bn | 20.29 Bn | 2.90 Bn | 315.00 Mn |
| 7 | Global Payments | 20.83 Bn | -735.18 Mn | 2.03 Bn | 757.58 Mn |
| 8 | Bitmine Immersion Technologies | 15.23 Bn | 12.61 Bn | 40.81 Mn | - |
| 9 | Guidewire Software | 12.47 Bn | 9.03 Bn | 269.68 Mn | - |
| 10 | Hyperscale Data | 80.79 Mn | -111.20 Mn | 8.80 Mn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2022 | 80,000.00 |
| Dec 31, 2021 | 79,000.00 |
| Sep 30, 2021 | 105,000.00 |
| Jun 30, 2021 | 87,000.00 |
| Mar 31, 2021 | 104,000.00 |
| Dec 31, 2020 | 84,000.00 |
| Sep 30, 2020 | 85,000.00 |
| Jun 30, 2020 | 83,715.00 |
| Mar 31, 2020 | 83,285.00 |
| Dec 31, 2019 | 101,995.00 |
| Sep 30, 2019 | 101,199.00 |
| Jun 30, 2019 | 137,047.00 |
| Mar 31, 2019 | 162,415.00 |
| Dec 31, 2018 | 359,582.00 |
| Sep 30, 2018 | 33,358.00 |
| Jun 30, 2018 | 33,358.00 |
| Mar 31, 2018 | 33,358.00 |
| Dec 31, 2017 | 54,335.00 |
| Sep 30, 2017 | 4,000.00 |
| Sep 30, 2015 | 18,000.00 |
API Access
Hyperscale Data Amortizatization of Intangibles 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=amortizatization-of-intangibles&ticker=GPUS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "amortizatization-of-intangibles", "ticker": "GPUS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=amortizatization-of-intangibles&ticker=GPUS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();