Correlation Between Eli Lilly and Hookipa Pharma
Can any of the company-specific risk be diversified away by investing in both Eli Lilly and Hookipa Pharma 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 Eli Lilly and Hookipa Pharma into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Eli Lilly and and Hookipa Pharma, you can compare the effects of market volatilities on Eli Lilly and Hookipa Pharma 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 Eli Lilly with a short position of Hookipa Pharma. Check out your portfolio center. Please also check ongoing floating volatility patterns of Eli Lilly and Hookipa Pharma.
Diversification Opportunities for Eli Lilly and Hookipa Pharma
-0.46 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Eli and Hookipa is -0.46. Overlapping area represents the amount of risk that can be diversified away by holding Eli Lilly and and Hookipa Pharma in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hookipa Pharma and Eli Lilly 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 Eli Lilly and are associated (or correlated) with Hookipa Pharma. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hookipa Pharma has no effect on the direction of Eli Lilly i.e., Eli Lilly and Hookipa Pharma go up and down completely randomly.
Pair Corralation between Eli Lilly and Hookipa Pharma
Considering the 90-day investment horizon Eli Lilly and is expected to generate 0.4 times more return on investment than Hookipa Pharma. However, Eli Lilly and is 2.48 times less risky than Hookipa Pharma. It trades about 0.06 of its potential returns per unit of risk. Hookipa Pharma is currently generating about -0.12 per unit of risk. If you would invest 78,182 in Eli Lilly and on December 27, 2024 and sell it today you would earn a total of 4,494 from holding Eli Lilly and or generate 5.75% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Eli Lilly and vs. Hookipa Pharma
Performance |
Timeline |
Eli Lilly |
Hookipa Pharma |
Eli Lilly and Hookipa Pharma Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Eli Lilly and Hookipa Pharma
The main advantage of trading using opposite Eli Lilly and Hookipa Pharma positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Eli Lilly position performs unexpectedly, Hookipa Pharma 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 Hookipa Pharma will offset losses from the drop in Hookipa Pharma's long position.Eli Lilly vs. Emergent Biosolutions | Eli Lilly vs. Bausch Health Companies | Eli Lilly vs. Neurocrine Biosciences | Eli Lilly vs. Teva Pharma Industries |
Hookipa Pharma vs. Mereo BioPharma Group | Hookipa Pharma vs. Terns Pharmaceuticals | Hookipa Pharma vs. PDS Biotechnology Corp | Hookipa Pharma vs. Inozyme Pharma |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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