Correlation Between Oncopeptides and Arise Windpower
Can any of the company-specific risk be diversified away by investing in both Oncopeptides and Arise Windpower 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 Oncopeptides and Arise Windpower into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Oncopeptides AB and Arise Windpower AB, you can compare the effects of market volatilities on Oncopeptides and Arise Windpower 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 Oncopeptides with a short position of Arise Windpower. Check out your portfolio center. Please also check ongoing floating volatility patterns of Oncopeptides and Arise Windpower.
Diversification Opportunities for Oncopeptides and Arise Windpower
0.44 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Oncopeptides and Arise is 0.44. Overlapping area represents the amount of risk that can be diversified away by holding Oncopeptides AB and Arise Windpower AB in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Arise Windpower AB and Oncopeptides 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 Oncopeptides AB are associated (or correlated) with Arise Windpower. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Arise Windpower AB has no effect on the direction of Oncopeptides i.e., Oncopeptides and Arise Windpower go up and down completely randomly.
Pair Corralation between Oncopeptides and Arise Windpower
Assuming the 90 days trading horizon Oncopeptides AB is expected to under-perform the Arise Windpower. In addition to that, Oncopeptides is 3.31 times more volatile than Arise Windpower AB. It trades about -0.16 of its total potential returns per unit of risk. Arise Windpower AB is currently generating about -0.24 per unit of volatility. If you would invest 4,075 in Arise Windpower AB on September 1, 2024 and sell it today you would lose (385.00) from holding Arise Windpower AB or give up 9.45% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 95.65% |
| Values | Daily Returns |
Oncopeptides AB vs. Arise Windpower AB
Performance |
| Timeline |
| Oncopeptides AB |
| Arise Windpower AB |
Oncopeptides and Arise Windpower Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Oncopeptides and Arise Windpower
The main advantage of trading using opposite Oncopeptides and Arise Windpower positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Oncopeptides position performs unexpectedly, Arise Windpower 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 Arise Windpower will offset losses from the drop in Arise Windpower's long position.| Oncopeptides vs. Hansa Biopharma AB | Oncopeptides vs. BioArctic AB | Oncopeptides vs. Sinch AB | Oncopeptides vs. Cantargia AB |
| Arise Windpower vs. Eolus Vind AB | Arise Windpower vs. SolTech Energy Sweden | Arise Windpower vs. Garo AB | Arise Windpower vs. NOTE AB |
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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