Compare Graham Net Net 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 Graham Net Net Analysis
Compare Where Food Comes, and Global Ship Lease Graham Net Net Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| WFCF | (8.6718) | 0.1508 | 0.1703 | (0.0303) | 0.2753 | 0.5252 | 0.1306 | 0.195 | (0.051) | (0.2424) | (0.1183) | 0.2289 | 0.1045 | (0.1232) | (0.14) | (0.13) |
Where Food Comes, and Global Ship Lease Graham Net Net description
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. | Business Services Invested over 100 shares | |
 | ESG Investing Invested few shares | |
 | Cash Cows Invested few shares | |
 | Blockchain Invested few shares | |
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 | Momentum Invested over 50 shares | |
 | Chemicals Invested over 40 shares | |
 | Banking Invested over 30 shares | |
 | FinTech 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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