Compare Revenue 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 Revenue Per Share Analysis
Compare Boxlight Corp Class, and KVH Industries Revenue Per Share Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BOXL | 6.4967 | 6.4967 | 6.4967 | 6.4967 | 6.4967 | 6.6162 | 37.9071 | 37.753 | 30.5109 | 24.7201 | 10.4063 | 25.1732 | 25.6568 | 18.6907 | 17.91 |
| KVHI | 2.485 | 7.6203 | 9.2787 | 10.7163 | 11.1927 | 11.8166 | 11.123 | 9.7502 | 10.0024 | 9.0436 | 8.9837 | 9.4289 | 7.4537 | 6.92 | 5.4 |
Boxlight Corp Class, and KVH Industries Revenue Per Share description
The amount of revenue generated by a company per share of stock, calculated by dividing total revenue by the average number of shares outstanding.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. | FinTech Invested over 40 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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