Compare Stock Based Compensation To Revenue 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 Stock Based Compensation To Revenue Analysis
Compare Brandywine Realty Trust, and HUBBELL INC 35 Stock Based Compensation To Revenue Over Time
Select Fundamental| 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BDN | 0.0107 | 0.0073 | 0.0057 | 0.0106 | 0.005 | 0.005 | 0.0065 | 0.0094 | 0.0105 | 0.0118 | 0.0124 | 0.0146 | 0.0177 | 0.0191 | 0.0172 | 0.0181 |
Brandywine Realty Trust, and HUBBELL INC 35 Stock Based Compensation To Revenue description
A metric that compares the total value of stock-based compensation granted by a company to its total revenue, indicating how much of the revenue is used to compensate employees with stock options or awards.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 | |
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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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