Compare Return On Tangible Assets Across Equities
You can use any or all of fundamental ratio historical patterns as a complementary method for asset selection as well as a tool for deciding entry and exit points. Many technical investors use fundamentals to limit their universe of possible positions. Check out your portfolio center.
Cross Equities Return On Tangible Assets Analysis
Compare Diageo PLC ADR, and Elevai Labs, Common Return On Tangible Assets Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DEO | 0.1068 | 0.1435 | 0.155 | 0.1491 | 0.1634 | 0.1392 | 0.1635 | 0.1763 | 0.1686 | 0.0788 | 0.1255 | 0.1601 | 0.1844 | 0.1596 | 0.0544 |
| ELAB | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (13.0954) | (1.1778) | (0.9514) | (0.8288) | (0.87) |
Diageo PLC ADR, and Elevai Labs, Common Return On Tangible Assets description
A profitability metric that measures a company's ability to generate earnings from its tangible assets.Investing Ideas
You can quickly originate your optimal portfoio using our predefined set of ideas and optimize them against your very unique investing style. A single investing idea is a collection of funds, stocks, ETFs, or cryptocurrencies that are programmatically selected from a pull of investment themes. After you determine your investment opportunity, you can then find an optimal portfolio that will maximize potential returns on the chosen idea or minimize its exposure to market volatility.Thematic Opportunities
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Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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