Compare Book Value Per Share 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 Book Value Per Share Analysis
Compare Drone Delivery Canada, and Dow Jones Industrial Book Value Per Share Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FLT | 0.1978 | 0.1978 | 0.138 | 0.1815 | 0.1116 | 0.0125 | 0.0177 | 0.1667 | 0.091 | 0.0899 | 0.1467 | 0.1385 | 0.0826 | 0.0433 | 0.0411 |
Drone Delivery Canada, and Dow Jones Industrial Book Value Per Share description
The ratio of equity available to common shareholders divided by the number of outstanding shares. This measure represents the value per share of a company according to its financial statements.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. | Entertainment Invested over 90 shares | |
 | Macroaxis Index Invested few shares | |
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 | Momentum Invested over 60 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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