Correlation Between EOSDAC and Bitcoin Gold
Can any of the company-specific risk be diversified away by investing in both EOSDAC and Bitcoin Gold 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 EOSDAC and Bitcoin Gold into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between EOSDAC and Bitcoin Gold, you can compare the effects of market volatilities on EOSDAC and Bitcoin Gold 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 EOSDAC with a short position of Bitcoin Gold. Check out your portfolio center. Please also check ongoing floating volatility patterns of EOSDAC and Bitcoin Gold.
Diversification Opportunities for EOSDAC and Bitcoin Gold
0.73 | Correlation Coefficient |
Poor diversification
The 3 months correlation between EOSDAC and Bitcoin is 0.73. Overlapping area represents the amount of risk that can be diversified away by holding EOSDAC and Bitcoin Gold in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bitcoin Gold and EOSDAC 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 EOSDAC are associated (or correlated) with Bitcoin Gold. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bitcoin Gold has no effect on the direction of EOSDAC i.e., EOSDAC and Bitcoin Gold go up and down completely randomly.
Pair Corralation between EOSDAC and Bitcoin Gold
Assuming the 90 days trading horizon EOSDAC is expected to generate 0.51 times more return on investment than Bitcoin Gold. However, EOSDAC is 1.96 times less risky than Bitcoin Gold. It trades about 0.08 of its potential returns per unit of risk. Bitcoin Gold is currently generating about -0.02 per unit of risk. If you would invest 0.03 in EOSDAC on November 1, 2024 and sell it today you would earn a total of 0.00 from holding EOSDAC or generate 7.59% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
EOSDAC vs. Bitcoin Gold
Performance |
| Timeline |
| EOSDAC |
| Bitcoin Gold |
EOSDAC and Bitcoin Gold Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with EOSDAC and Bitcoin Gold
The main advantage of trading using opposite EOSDAC and Bitcoin Gold positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if EOSDAC position performs unexpectedly, Bitcoin Gold 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 Bitcoin Gold will offset losses from the drop in Bitcoin Gold's long position.The idea behind EOSDAC and Bitcoin Gold 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.| Bitcoin Gold vs. Bitcoin Cash | Bitcoin Gold vs. Bitcoin SV | Bitcoin Gold vs. Staked Ether | Bitcoin Gold vs. Phala Network |
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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