Correlation Between Lonza and BioMark Diagnostics
Can any of the company-specific risk be diversified away by investing in both Lonza and BioMark Diagnostics 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 Lonza and BioMark Diagnostics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lonza Group and BioMark Diagnostics, you can compare the effects of market volatilities on Lonza and BioMark Diagnostics 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 Lonza with a short position of BioMark Diagnostics. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lonza and BioMark Diagnostics.
Diversification Opportunities for Lonza and BioMark Diagnostics
0.6 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Lonza and BioMark is 0.6. Overlapping area represents the amount of risk that can be diversified away by holding Lonza Group and BioMark Diagnostics in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BioMark Diagnostics and Lonza 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 Lonza Group are associated (or correlated) with BioMark Diagnostics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BioMark Diagnostics has no effect on the direction of Lonza i.e., Lonza and BioMark Diagnostics go up and down completely randomly.
Pair Corralation between Lonza and BioMark Diagnostics
Assuming the 90 days horizon Lonza is expected to generate 1.91 times less return on investment than BioMark Diagnostics. But when comparing it to its historical volatility, Lonza Group is 2.05 times less risky than BioMark Diagnostics. It trades about 0.03 of its potential returns per unit of risk. BioMark Diagnostics is currently generating about 0.03 of returns per unit of risk over similar time horizon. If you would invest 14.00 in BioMark Diagnostics on September 14, 2024 and sell it today you would earn a total of 1.00 from holding BioMark Diagnostics or generate 7.14% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 99.6% |
| Values | Daily Returns |
Lonza Group vs. BioMark Diagnostics
Performance |
| Timeline |
| Lonza Group |
| BioMark Diagnostics |
Lonza and BioMark Diagnostics Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Lonza and BioMark Diagnostics
The main advantage of trading using opposite Lonza and BioMark Diagnostics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lonza position performs unexpectedly, BioMark Diagnostics 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 BioMark Diagnostics will offset losses from the drop in BioMark Diagnostics' long position.| Lonza vs. China New Energy | Lonza vs. Sonic Healthcare Ltd | Lonza vs. Charles River Laboratories | Lonza vs. Qiagen NV |
| BioMark Diagnostics vs. Neuronetics | BioMark Diagnostics vs. Intelligent Bio Solutions | BioMark Diagnostics vs. Biodesix | BioMark Diagnostics vs. Precipio |
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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