Correlation Between Swell Network and Wormhole
Can any of the company-specific risk be diversified away by investing in both Swell Network and Wormhole 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 Swell Network and Wormhole into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Swell Network and Wormhole, you can compare the effects of market volatilities on Swell Network and Wormhole 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 Swell Network with a short position of Wormhole. Check out your portfolio center. Please also check ongoing floating volatility patterns of Swell Network and Wormhole.
Diversification Opportunities for Swell Network and Wormhole
-0.22 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Swell and Wormhole is -0.22. Overlapping area represents the amount of risk that can be diversified away by holding Swell Network and Wormhole in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wormhole and Swell Network 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 Swell Network are associated (or correlated) with Wormhole. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wormhole has no effect on the direction of Swell Network i.e., Swell Network and Wormhole go up and down completely randomly.
Pair Corralation between Swell Network and Wormhole
Assuming the 90 days trading horizon Swell Network is expected to under-perform the Wormhole. But the crypto coin apears to be less risky and, when comparing its historical volatility, Swell Network is 1.02 times less risky than Wormhole. The crypto coin trades about -0.51 of its potential returns per unit of risk. The Wormhole is currently generating about -0.26 of returns per unit of risk over similar time horizon. If you would invest 26.00 in Wormhole on November 8, 2024 and sell it today you would lose (10.00) from holding Wormhole or give up 38.46% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Swell Network vs. Wormhole
Performance |
| Timeline |
| Swell Network |
| Wormhole |
Swell Network and Wormhole Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Swell Network and Wormhole
The main advantage of trading using opposite Swell Network and Wormhole positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Swell Network position performs unexpectedly, Wormhole 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 Wormhole will offset losses from the drop in Wormhole's long position.The idea behind Swell Network and Wormhole 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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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