Correlation Between Chainlink and Bitcoin SV
Can any of the company-specific risk be diversified away by investing in both Chainlink and Bitcoin SV 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 Chainlink and Bitcoin SV into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chainlink and Bitcoin SV, you can compare the effects of market volatilities on Chainlink and Bitcoin SV 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 Chainlink with a short position of Bitcoin SV. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chainlink and Bitcoin SV.
Diversification Opportunities for Chainlink and Bitcoin SV
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Chainlink and Bitcoin is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Chainlink and Bitcoin SV in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bitcoin SV and Chainlink 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 Chainlink are associated (or correlated) with Bitcoin SV. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bitcoin SV has no effect on the direction of Chainlink i.e., Chainlink and Bitcoin SV go up and down completely randomly.
Pair Corralation between Chainlink and Bitcoin SV
Assuming the 90 days trading horizon Chainlink is expected to generate 0.74 times more return on investment than Bitcoin SV. However, Chainlink is 1.35 times less risky than Bitcoin SV. It trades about -0.25 of its potential returns per unit of risk. Bitcoin SV is currently generating about -0.22 per unit of risk. If you would invest 1,915 in Chainlink on January 27, 2024 and sell it today you would lose (478.00) from holding Chainlink or give up 24.96% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Chainlink vs. Bitcoin SV
Performance |
Timeline |
Chainlink |
Bitcoin SV |
Chainlink and Bitcoin SV Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chainlink and Bitcoin SV
The main advantage of trading using opposite Chainlink and Bitcoin SV positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chainlink position performs unexpectedly, Bitcoin SV 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 SV will offset losses from the drop in Bitcoin SV's long position.The idea behind Chainlink and Bitcoin SV 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 SV vs. Bitcoin Gold | Bitcoin SV vs. Bitcoin Cash | Bitcoin SV vs. Staked Ether | Bitcoin SV vs. XCAD 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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