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.
Cross Equities Stock Based Compensation To Revenue Analysis
Compare Dyadic International, and Rohm Co 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DYAI | 101 | 0.0769 | 0.0769 | 0.0471 | 0.0786 | 3.5165 | 1.0538 | 0.8484 | 0.3606 | 0.6966 | 1.0312 | 0.7422 | 0.6446 | 0.4292 | 0.49 | 0.47 |
Dyadic International, and Rohm Co 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. | Business Services Invested over 100 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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