Pure Storage

ISIN US74624M1027

WKN A14YFN

Overview

Quote

GBP 28.69

04/04/2025 (gettex)

-2.50|-8.02%

daily change

52 weeks low/high

28.69

58.72

Description

Pure Storage, Inc. engages in the business of delivering innovative and disruptive data storage, products, and services that enable customers to maximize the value of data. The firm is also involved in the provision of data storage and management with a mission to redefine the storage experience by simplifying how people consume and interact with data. The company was founded by John M. Hayes and John Colgrove in October 2009 and is headquartered in Santa Clara, CA.

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Technology Hardware Computer Hardware and Storage United States

Chart

1 year: -31.12%

date: 04/04/2025

04/04/2024 - 04/04/2025

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Financials

Key metrics

Market capitalisation, EUR	11,978.25 m
EPS, EUR	0.28
P/B ratio	10.20
P/E ratio	131.14
Dividend yield	0.00%

Income statement (2024)

Revenue, EUR	2,942.21 m
Net income, EUR	99.13 m
Profit margin	3.37%

What ETF is Pure Storage in?

There are 165 ETFs which contain Pure Storage. All of these ETFs are listed in the table below. The ETF with the largest weighting of Pure Storage is the First Trust Cloud Computing UCITS ETF Acc.

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ETF	Weight	Investment focus	Holdings	TER	Fund size (in m EUR)	Return 1Y	ISIN
First Trust Cloud Computing UCITS ETF Acc	3.98%	Equity World Technology Cloud Technology	61	0.60%	346	-3.23%	IE00BFD2H405
Invesco Artificial Intelligence Enablers UCITS ETF Acc	3.43%	Equity World Technology Social/Environmental Artificial Intelligence	33	0.35%	7	-	IE000LGWDNE5
iShares AI Innovation Active UCITS ETF USD (Acc)	1.97%	Equity World Social/Environmental Artificial Intelligence	37	0.73%	18	-	IE000G0E83X3
L&G Artificial Intelligence UCITS ETF	1.80%	Equity World Technology Social/Environmental Artificial Intelligence	55	0.49%	699	-7.04%	IE00BK5BCD43
ARK Artificial Intelligence & Robotics UCITS ETF USD Accumulating	1.78%	Equity World Artificial Intelligence	41	0.75%	49	-	IE0003A512E4
iShares Metaverse UCITS ETF USD (Acc)	0.96%	Equity World Social/Environmental Metaverse	75	0.50%	116	-3.39%	IE000RN58M26
Amundi MSCI Robotics & AI UCITS ETF Acc	0.74%	Equity World Technology Social/Environmental Artificial Intelligence	151	0.40%	839	-9.73%	LU1861132840
SPDR S&P 400 US Mid Cap UCITS ETF	0.56%	Equity United States Mid Cap	401	0.30%	5,023	-10.10%	IE00B4YBJ215
iShares AI Infrastructure UCITS ETF USD (Acc)	0.47%	Equity World Social/Environmental Artificial Intelligence	40	0.35%	56	-	IE000X59ZHE2
iShares MSCI USA Mid-Cap Equal Weight UCITS ETF USD (Acc)	0.34%	Equity United States Mid Cap	330	0.20%	283	-4.85%	IE00BD1F4K20

Showing 1 to 10 of 165 entries

1 2 3 4 5…17

Performance

Returns overview

YTD	-42.39%
1 month	-27.35%
3 months	-44.47%
6 months	-26.49%
1 year	-31.12%
3 years	+6.46%
5 years	+232.83%
Since inception (MAX)	+127.34%
2024	+75.91%
2023	+25.04%
2022	-6.98%
2021	+43.18%

Monthly returns in a heat map

Risk

Risk metrics in this section:

Volatility, annualised, measured for 1, 3 and 5 year periods. The annualised volatility reflects the degree of price fluctuations during a one year period. The higher the volatility, the more significantly the price of the asset (stock, ETF, etc.) has changed in the past. Assets with higher volatility are generally considered more risky. We calculate the volatility based on the data for the past 1, 3 and 5 years so that you can see if price fluctuations for the ETF became stronger or weaker over time.
Return per risk for 1, 3 and 5 year periods. This is the annualised (i.e. converted to a one year period) past return divided by the past annualised volatility. The metric puts the historical return of an asset in relation to its historical risk and gives you a retrospective indication of the degree of price fluctuation you had to bear with in order to obtain the return. We calculate this parameter for 1, 3 and 5 year periods to display its evolution over time.
Maximum drawdown for a period. This shows the worst possible loss an investor could have suffered during the respective period, by first buying and subsequently selling the asset at the least favourable prices. For example, if there was the following sequence of daily ETF prices: 10€, 5€, 12€, 20€, an investor would have suffered the worst loss by buying for 10€ and subsequently selling for 5€. Therefore in this case the maximum drawdown would be (5€ - 10€)/10€ = -50%.

ETF returns include dividend payments (if applicable).