Correlation Between Ironwood Pharmaceuticals and OptiNose
Can any of the company-specific risk be diversified away by investing in both Ironwood Pharmaceuticals and OptiNose 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 Ironwood Pharmaceuticals and OptiNose into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ironwood Pharmaceuticals and OptiNose, you can compare the effects of market volatilities on Ironwood Pharmaceuticals and OptiNose 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 Ironwood Pharmaceuticals with a short position of OptiNose. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ironwood Pharmaceuticals and OptiNose.
Diversification Opportunities for Ironwood Pharmaceuticals and OptiNose
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Ironwood and OptiNose is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Ironwood Pharmaceuticals and OptiNose in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on OptiNose and Ironwood Pharmaceuticals 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 Ironwood Pharmaceuticals are associated (or correlated) with OptiNose. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of OptiNose has no effect on the direction of Ironwood Pharmaceuticals i.e., Ironwood Pharmaceuticals and OptiNose go up and down completely randomly.
Pair Corralation between Ironwood Pharmaceuticals and OptiNose
Given the investment horizon of 90 days Ironwood Pharmaceuticals is expected to generate 0.57 times more return on investment than OptiNose. However, Ironwood Pharmaceuticals is 1.76 times less risky than OptiNose. It trades about -0.16 of its potential returns per unit of risk. OptiNose is currently generating about -0.14 per unit of risk. If you would invest 415.00 in Ironwood Pharmaceuticals on August 28, 2024 and sell it today you would lose (80.00) from holding Ironwood Pharmaceuticals or give up 19.28% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ironwood Pharmaceuticals vs. OptiNose
Performance |
| Timeline |
| Ironwood Pharmaceuticals |
| OptiNose |
Ironwood Pharmaceuticals and OptiNose Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ironwood Pharmaceuticals and OptiNose
The main advantage of trading using opposite Ironwood Pharmaceuticals and OptiNose positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ironwood Pharmaceuticals position performs unexpectedly, OptiNose 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 OptiNose will offset losses from the drop in OptiNose's long position.| Ironwood Pharmaceuticals vs. Neurocrine Biosciences | Ironwood Pharmaceuticals vs. Amphastar P | Ironwood Pharmaceuticals vs. Collegium Pharmaceutical | Ironwood Pharmaceuticals vs. ANI 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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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