Correlation Between Alkami Technology and Jfrog
Can any of the company-specific risk be diversified away by investing in both Alkami Technology and Jfrog at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Alkami Technology and Jfrog into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Alkami Technology and Jfrog, you can compare the effects of market volatilities on Alkami Technology and Jfrog and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Alkami Technology with a short position of Jfrog. Check out your portfolio center. Please also check ongoing floating volatility patterns of Alkami Technology and Jfrog.
Diversification Opportunities for Alkami Technology and Jfrog
0.72 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Alkami and Jfrog is 0.72. Overlapping area represents the amount of risk that can be diversified away by holding Alkami Technology and Jfrog in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Jfrog and Alkami Technology is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Alkami Technology are associated (or correlated) with Jfrog. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Jfrog has no effect on the direction of Alkami Technology i.e., Alkami Technology and Jfrog go up and down completely randomly.
Pair Corralation between Alkami Technology and Jfrog
Given the investment horizon of 90 days Alkami Technology is expected to generate 2.1 times less return on investment than Jfrog. In addition to that, Alkami Technology is 1.33 times more volatile than Jfrog. It trades about 0.05 of its total potential returns per unit of risk. Jfrog is currently generating about 0.13 per unit of volatility. If you would invest 2,932 in Jfrog on August 29, 2024 and sell it today you would earn a total of 201.00 from holding Jfrog or generate 6.86% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Alkami Technology vs. Jfrog
Performance |
| Timeline |
| Alkami Technology |
| Jfrog |
Alkami Technology and Jfrog Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Alkami Technology and Jfrog
The main advantage of trading using opposite Alkami Technology and Jfrog positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Alkami Technology position performs unexpectedly, Jfrog can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Jfrog will offset losses from the drop in Jfrog's long position.The idea behind Alkami Technology and Jfrog pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.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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