Correlation Between Krystal Biotech and Atea Pharmaceuticals
Can any of the company-specific risk be diversified away by investing in both Krystal Biotech and Atea Pharmaceuticals 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 Krystal Biotech and Atea Pharmaceuticals into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Krystal Biotech and Atea Pharmaceuticals, you can compare the effects of market volatilities on Krystal Biotech and Atea Pharmaceuticals 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 Krystal Biotech with a short position of Atea Pharmaceuticals. Check out your portfolio center. Please also check ongoing floating volatility patterns of Krystal Biotech and Atea Pharmaceuticals.
Diversification Opportunities for Krystal Biotech and Atea Pharmaceuticals
0.65 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Krystal and Atea is 0.65. Overlapping area represents the amount of risk that can be diversified away by holding Krystal Biotech and Atea Pharmaceuticals in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Atea Pharmaceuticals and Krystal Biotech 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 Krystal Biotech are associated (or correlated) with Atea Pharmaceuticals. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Atea Pharmaceuticals has no effect on the direction of Krystal Biotech i.e., Krystal Biotech and Atea Pharmaceuticals go up and down completely randomly.
Pair Corralation between Krystal Biotech and Atea Pharmaceuticals
Given the investment horizon of 90 days Krystal Biotech is expected to generate 1.19 times more return on investment than Atea Pharmaceuticals. However, Krystal Biotech is 1.19 times more volatile than Atea Pharmaceuticals. It trades about 0.14 of its potential returns per unit of risk. Atea Pharmaceuticals is currently generating about 0.01 per unit of risk. If you would invest 17,498 in Krystal Biotech on August 25, 2024 and sell it today you would earn a total of 1,542 from holding Krystal Biotech or generate 8.81% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Krystal Biotech vs. Atea Pharmaceuticals
Performance |
Timeline |
Krystal Biotech |
Atea Pharmaceuticals |
Krystal Biotech and Atea Pharmaceuticals Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Krystal Biotech and Atea Pharmaceuticals
The main advantage of trading using opposite Krystal Biotech and Atea Pharmaceuticals positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Krystal Biotech position performs unexpectedly, Atea Pharmaceuticals 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 Atea Pharmaceuticals will offset losses from the drop in Atea Pharmaceuticals' long position.Krystal Biotech vs. Eliem Therapeutics | Krystal Biotech vs. HCW Biologics | Krystal Biotech vs. Scpharmaceuticals | Krystal Biotech vs. Milestone 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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