Correlation Between Golem Network and Sushi
Can any of the company-specific risk be diversified away by investing in both Golem Network and Sushi 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 Golem Network and Sushi into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Golem Network Token and Sushi, you can compare the effects of market volatilities on Golem Network and Sushi 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 Golem Network with a short position of Sushi. Check out your portfolio center. Please also check ongoing floating volatility patterns of Golem Network and Sushi.
Diversification Opportunities for Golem Network and Sushi
0.95 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Golem and Sushi is 0.95. Overlapping area represents the amount of risk that can be diversified away by holding Golem Network Token and Sushi in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sushi and Golem 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 Golem Network Token are associated (or correlated) with Sushi. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sushi has no effect on the direction of Golem Network i.e., Golem Network and Sushi go up and down completely randomly.
Pair Corralation between Golem Network and Sushi
Assuming the 90 days trading horizon Golem Network is expected to generate 1.43 times less return on investment than Sushi. But when comparing it to its historical volatility, Golem Network Token is 1.12 times less risky than Sushi. It trades about 0.18 of its potential returns per unit of risk. Sushi is currently generating about 0.23 of returns per unit of risk over similar time horizon. If you would invest 55.00 in Sushi on September 1, 2024 and sell it today you would earn a total of 79.00 from holding Sushi or generate 143.64% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Golem Network Token vs. Sushi
Performance |
Timeline |
Golem Network Token |
Sushi |
Golem Network and Sushi Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Golem Network and Sushi
The main advantage of trading using opposite Golem Network and Sushi positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Golem Network position performs unexpectedly, Sushi 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 Sushi will offset losses from the drop in Sushi's long position.The idea behind Golem Network Token and Sushi 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 Equity Valuation module to check real value of public entities based on technical and fundamental data.
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