Correlation Between Sunndal Sparebank and Clean Seas
Can any of the company-specific risk be diversified away by investing in both Sunndal Sparebank and Clean Seas 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 Sunndal Sparebank and Clean Seas into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sunndal Sparebank and Clean Seas Seafood, you can compare the effects of market volatilities on Sunndal Sparebank and Clean Seas 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 Sunndal Sparebank with a short position of Clean Seas. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sunndal Sparebank and Clean Seas.
Diversification Opportunities for Sunndal Sparebank and Clean Seas
-0.15 | Correlation Coefficient |
Good diversification
The 3 months correlation between Sunndal and Clean is -0.15. Overlapping area represents the amount of risk that can be diversified away by holding Sunndal Sparebank and Clean Seas Seafood in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Clean Seas Seafood and Sunndal Sparebank 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 Sunndal Sparebank are associated (or correlated) with Clean Seas. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Clean Seas Seafood has no effect on the direction of Sunndal Sparebank i.e., Sunndal Sparebank and Clean Seas go up and down completely randomly.
Pair Corralation between Sunndal Sparebank and Clean Seas
Assuming the 90 days trading horizon Sunndal Sparebank is expected to generate 0.29 times more return on investment than Clean Seas. However, Sunndal Sparebank is 3.47 times less risky than Clean Seas. It trades about 0.02 of its potential returns per unit of risk. Clean Seas Seafood is currently generating about -0.06 per unit of risk. If you would invest 10,675 in Sunndal Sparebank on November 2, 2024 and sell it today you would earn a total of 973.00 from holding Sunndal Sparebank or generate 9.11% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 99.8% |
| Values | Daily Returns |
Sunndal Sparebank vs. Clean Seas Seafood
Performance |
| Timeline |
| Sunndal Sparebank |
| Clean Seas Seafood |
Sunndal Sparebank and Clean Seas Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sunndal Sparebank and Clean Seas
The main advantage of trading using opposite Sunndal Sparebank and Clean Seas positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sunndal Sparebank position performs unexpectedly, Clean Seas 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 Clean Seas will offset losses from the drop in Clean Seas' long position.| Sunndal Sparebank vs. Odfjell Technology | Sunndal Sparebank vs. Nordic Semiconductor ASA | Sunndal Sparebank vs. Eidesvik Offshore ASA | Sunndal Sparebank vs. Sparebanken Ost |
| Clean Seas vs. Masoval AS | Clean Seas vs. Andfjord Salmon AS | Clean Seas vs. Arctic Fish Holding | Clean Seas vs. Ice Fish Farm |
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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