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 Commerce Bancshares, and AviChina Industry Graham Net Net Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CBSH | (27.4029) | (55.6077) | (64.8932) | (72.7289) | (76.9299) | (86.5779) | (94.4802) | (95.2141) | (94.6724) | 68.3754 | 109 | 140 | (124) | (141) | (134) |
Commerce Bancshares, and AviChina Industry 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. | Investor Favorites Invested over 40 shares | |
 | Hedge Favorites Invested over 50 shares | |
 | Automobiles and Trucks Invested over 200 shares | |
 | Driverless Cars Invested over 50 shares | |
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 | Impulse Invested few shares | |
 | Macroaxis Index Invested few shares | |
 | Millennials Best Invested few shares | |
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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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