Correlation Between Kinetics Internet and Red Oak
Can any of the company-specific risk be diversified away by investing in both Kinetics Internet and Red Oak 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 Kinetics Internet and Red Oak into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kinetics Internet Fund and Red Oak Technology, you can compare the effects of market volatilities on Kinetics Internet and Red Oak 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 Kinetics Internet with a short position of Red Oak. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kinetics Internet and Red Oak.
Diversification Opportunities for Kinetics Internet and Red Oak
0.61 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Kinetics and Red is 0.61. Overlapping area represents the amount of risk that can be diversified away by holding Kinetics Internet Fund and Red Oak Technology in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Red Oak Technology and Kinetics Internet 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 Kinetics Internet Fund are associated (or correlated) with Red Oak. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Red Oak Technology has no effect on the direction of Kinetics Internet i.e., Kinetics Internet and Red Oak go up and down completely randomly.
Pair Corralation between Kinetics Internet and Red Oak
Assuming the 90 days horizon Kinetics Internet Fund is expected to generate 1.48 times more return on investment than Red Oak. However, Kinetics Internet is 1.48 times more volatile than Red Oak Technology. It trades about 0.17 of its potential returns per unit of risk. Red Oak Technology is currently generating about 0.08 per unit of risk. If you would invest 5,864 in Kinetics Internet Fund on August 25, 2024 and sell it today you would earn a total of 6,179 from holding Kinetics Internet Fund or generate 105.37% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Kinetics Internet Fund vs. Red Oak Technology
Performance |
| Timeline |
| Kinetics Internet |
| Red Oak Technology |
Kinetics Internet and Red Oak Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Kinetics Internet and Red Oak
The main advantage of trading using opposite Kinetics Internet and Red Oak positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kinetics Internet position performs unexpectedly, Red Oak 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 Red Oak will offset losses from the drop in Red Oak's long position.| Kinetics Internet vs. Kinetics Global Fund | Kinetics Internet vs. Kinetics Global Fund | Kinetics Internet vs. Kinetics Paradigm Fund | Kinetics Internet vs. Kinetics Global Fund |
| Red Oak vs. Pin Oak Equity | Red Oak vs. White Oak Select | Red Oak vs. Black Oak Emerging | Red Oak vs. Berkshire Focus |
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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