Correlation Between Hub Cyber and AppTech Payments
Can any of the company-specific risk be diversified away by investing in both Hub Cyber and AppTech Payments 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 Hub Cyber and AppTech Payments into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hub Cyber Security and AppTech Payments Corp, you can compare the effects of market volatilities on Hub Cyber and AppTech Payments 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 Hub Cyber with a short position of AppTech Payments. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hub Cyber and AppTech Payments.
Diversification Opportunities for Hub Cyber and AppTech Payments
-0.03 | Correlation Coefficient |
Good diversification
The 3 months correlation between Hub and AppTech is -0.03. Overlapping area represents the amount of risk that can be diversified away by holding Hub Cyber Security and AppTech Payments Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AppTech Payments Corp and Hub Cyber 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 Hub Cyber Security are associated (or correlated) with AppTech Payments. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AppTech Payments Corp has no effect on the direction of Hub Cyber i.e., Hub Cyber and AppTech Payments go up and down completely randomly.
Pair Corralation between Hub Cyber and AppTech Payments
Given the investment horizon of 90 days Hub Cyber Security is expected to under-perform the AppTech Payments. But the stock apears to be less risky and, when comparing its historical volatility, Hub Cyber Security is 1.04 times less risky than AppTech Payments. The stock trades about -0.05 of its potential returns per unit of risk. The AppTech Payments Corp is currently generating about 0.13 of returns per unit of risk over similar time horizon. If you would invest 11.00 in AppTech Payments Corp on December 28, 2024 and sell it today you would earn a total of 7.00 from holding AppTech Payments Corp or generate 63.64% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Hub Cyber Security vs. AppTech Payments Corp
Performance |
Timeline |
Hub Cyber Security |
AppTech Payments Corp |
Hub Cyber and AppTech Payments Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hub Cyber and AppTech Payments
The main advantage of trading using opposite Hub Cyber and AppTech Payments positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hub Cyber position performs unexpectedly, AppTech Payments 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 AppTech Payments will offset losses from the drop in AppTech Payments' long position.Hub Cyber vs. authID Inc | Hub Cyber vs. VirnetX Holding Corp | Hub Cyber vs. Aurora Mobile | Hub Cyber vs. GigaCloud Technology Class |
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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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