Correlation Between SAB Biotherapeutics and Bionano Genomics
Can any of the company-specific risk be diversified away by investing in both SAB Biotherapeutics and Bionano Genomics 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 SAB Biotherapeutics and Bionano Genomics into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SAB Biotherapeutics and Bionano Genomics, you can compare the effects of market volatilities on SAB Biotherapeutics and Bionano Genomics 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 SAB Biotherapeutics with a short position of Bionano Genomics. Check out your portfolio center. Please also check ongoing floating volatility patterns of SAB Biotherapeutics and Bionano Genomics.
Diversification Opportunities for SAB Biotherapeutics and Bionano Genomics
-0.74 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between SAB and Bionano is -0.74. Overlapping area represents the amount of risk that can be diversified away by holding SAB Biotherapeutics and Bionano Genomics in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bionano Genomics and SAB Biotherapeutics 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 SAB Biotherapeutics are associated (or correlated) with Bionano Genomics. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bionano Genomics has no effect on the direction of SAB Biotherapeutics i.e., SAB Biotherapeutics and Bionano Genomics go up and down completely randomly.
Pair Corralation between SAB Biotherapeutics and Bionano Genomics
Assuming the 90 days horizon SAB Biotherapeutics is expected to generate 8.75 times more return on investment than Bionano Genomics. However, SAB Biotherapeutics is 8.75 times more volatile than Bionano Genomics. It trades about 0.11 of its potential returns per unit of risk. Bionano Genomics is currently generating about -0.19 per unit of risk. If you would invest 6.88 in SAB Biotherapeutics on September 25, 2024 and sell it today you would lose (1.88) from holding SAB Biotherapeutics or give up 27.33% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 95.24% |
Values | Daily Returns |
SAB Biotherapeutics vs. Bionano Genomics
Performance |
Timeline |
SAB Biotherapeutics |
Bionano Genomics |
SAB Biotherapeutics and Bionano Genomics Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SAB Biotherapeutics and Bionano Genomics
The main advantage of trading using opposite SAB Biotherapeutics and Bionano Genomics positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SAB Biotherapeutics position performs unexpectedly, Bionano Genomics 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 Bionano Genomics will offset losses from the drop in Bionano Genomics' long position.SAB Biotherapeutics vs. Fate Therapeutics | SAB Biotherapeutics vs. Caribou Biosciences | SAB Biotherapeutics vs. Karyopharm Therapeutics | SAB Biotherapeutics vs. X4 Pharmaceuticals |
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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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