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.
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Cross Equities Return On Tangible Assets Analysis
Compare Alphabet Class C, and CROWN CASTLE INTERNA Return On Tangible Assets Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GOOG | (0.3134) | 0.153 | 0.1417 | 0.1364 | 0.1274 | 0.128 | 0.1319 | 0.0712 | 0.1445 | 0.1356 | 0.1356 | 0.227 | 0.1794 | 0.1977 | 0.18 | 0.11 |
Alphabet Class C, and CROWN CASTLE INTERNA Return On Tangible Assets description
A profitability metric that measures a company's ability to generate earnings from its tangible assets.Trending Themes
If you are a self-driven investor, you will appreciate our idea-generating investing themes. Our themes help you align your investments inspirations with your core values and are essential building blocks of your portfolios. A typical investing theme is an unweighted collection of up to 20 funds, stocks, ETFs, or cryptocurrencies that are programmatically selected from a pull of equities with common characteristics such as industry and growth potential, volatility, or market segment. | USD Crypto Fund Invested less than 1 share | |
 | Macroaxis Index Invested over 10K shares | |
 | Manufacturing Invested over 40 shares | |
 | Diversified Assets Invested over 40 shares |
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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