Correlation Between Kingsoft Cloud and Expensify
Can any of the company-specific risk be diversified away by investing in both Kingsoft Cloud and Expensify 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 Kingsoft Cloud and Expensify into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kingsoft Cloud Holdings and Expensify, you can compare the effects of market volatilities on Kingsoft Cloud and Expensify 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 Kingsoft Cloud with a short position of Expensify. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kingsoft Cloud and Expensify.
Diversification Opportunities for Kingsoft Cloud and Expensify
0.24 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Kingsoft and Expensify is 0.24. Overlapping area represents the amount of risk that can be diversified away by holding Kingsoft Cloud Holdings and Expensify in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Expensify and Kingsoft Cloud 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 Kingsoft Cloud Holdings are associated (or correlated) with Expensify. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Expensify has no effect on the direction of Kingsoft Cloud i.e., Kingsoft Cloud and Expensify go up and down completely randomly.
Pair Corralation between Kingsoft Cloud and Expensify
Allowing for the 90-day total investment horizon Kingsoft Cloud Holdings is expected to generate 1.65 times more return on investment than Expensify. However, Kingsoft Cloud is 1.65 times more volatile than Expensify. It trades about 0.08 of its potential returns per unit of risk. Expensify is currently generating about 0.0 per unit of risk. If you would invest 1,146 in Kingsoft Cloud Holdings on December 29, 2024 and sell it today you would earn a total of 251.00 from holding Kingsoft Cloud Holdings or generate 21.9% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Kingsoft Cloud Holdings vs. Expensify
Performance |
Timeline |
Kingsoft Cloud Holdings |
Expensify |
Kingsoft Cloud and Expensify Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Kingsoft Cloud and Expensify
The main advantage of trading using opposite Kingsoft Cloud and Expensify positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kingsoft Cloud position performs unexpectedly, Expensify 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 Expensify will offset losses from the drop in Expensify's long position.Kingsoft Cloud vs. Oneconnect Financial Technology | Kingsoft Cloud vs. Global Business Travel | Kingsoft Cloud vs. Alight Inc | Kingsoft Cloud vs. CS Disco LLC |
Expensify vs. Clearwater Analytics Holdings | Expensify vs. Sprinklr | Expensify vs. Alkami Technology | Expensify vs. Vertex |
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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