Correlation Between Bitcoin and Eli Lilly
Can any of the company-specific risk be diversified away by investing in both Bitcoin and Eli Lilly 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 Bitcoin and Eli Lilly into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bitcoin and Eli Lilly and, you can compare the effects of market volatilities on Bitcoin and Eli Lilly 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 Bitcoin with a short position of Eli Lilly. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bitcoin and Eli Lilly.
Diversification Opportunities for Bitcoin and Eli Lilly
Pay attention - limited upside
The 3 months correlation between Bitcoin and Eli is -0.72. Overlapping area represents the amount of risk that can be diversified away by holding Bitcoin and Eli Lilly and in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Eli Lilly and Bitcoin 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 Bitcoin are associated (or correlated) with Eli Lilly. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Eli Lilly has no effect on the direction of Bitcoin i.e., Bitcoin and Eli Lilly go up and down completely randomly.
Pair Corralation between Bitcoin and Eli Lilly
Assuming the 90 days trading horizon Bitcoin is expected to generate 1.62 times more return on investment than Eli Lilly. However, Bitcoin is 1.62 times more volatile than Eli Lilly and. It trades about 0.02 of its potential returns per unit of risk. Eli Lilly and is currently generating about -0.22 per unit of risk. If you would invest 9,665,788 in Bitcoin on October 10, 2024 and sell it today you would earn a total of 34,549 from holding Bitcoin or generate 0.36% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 80.95% |
Values | Daily Returns |
Bitcoin vs. Eli Lilly and
Performance |
Timeline |
Bitcoin |
Eli Lilly |
Bitcoin and Eli Lilly Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bitcoin and Eli Lilly
The main advantage of trading using opposite Bitcoin and Eli Lilly positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bitcoin position performs unexpectedly, Eli Lilly 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 Eli Lilly will offset losses from the drop in Eli Lilly's long position.The idea behind Bitcoin and Eli Lilly and 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.Eli Lilly vs. Platinum Investment Management | Eli Lilly vs. Brockhaus Capital Management | Eli Lilly vs. China BlueChemical | Eli Lilly vs. Q2M Managementberatung AG |
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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