Correlation Between Sogn Sparebank and Sea1 Offshore
Can any of the company-specific risk be diversified away by investing in both Sogn Sparebank and Sea1 Offshore 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 Sogn Sparebank and Sea1 Offshore into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sogn Sparebank and Sea1 Offshore, you can compare the effects of market volatilities on Sogn Sparebank and Sea1 Offshore 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 Sogn Sparebank with a short position of Sea1 Offshore. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sogn Sparebank and Sea1 Offshore.
Diversification Opportunities for Sogn Sparebank and Sea1 Offshore
0.85 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Sogn and Sea1 is 0.85. Overlapping area represents the amount of risk that can be diversified away by holding Sogn Sparebank and Sea1 Offshore in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sea1 Offshore and Sogn 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 Sogn Sparebank are associated (or correlated) with Sea1 Offshore. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sea1 Offshore has no effect on the direction of Sogn Sparebank i.e., Sogn Sparebank and Sea1 Offshore go up and down completely randomly.
Pair Corralation between Sogn Sparebank and Sea1 Offshore
Assuming the 90 days trading horizon Sogn Sparebank is expected to generate 2.78 times less return on investment than Sea1 Offshore. But when comparing it to its historical volatility, Sogn Sparebank is 1.26 times less risky than Sea1 Offshore. It trades about 0.11 of its potential returns per unit of risk. Sea1 Offshore is currently generating about 0.24 of returns per unit of risk over similar time horizon. If you would invest 1,860 in Sea1 Offshore on April 24, 2025 and sell it today you would earn a total of 755.00 from holding Sea1 Offshore or generate 40.59% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Sogn Sparebank vs. Sea1 Offshore
Performance |
Timeline |
Sogn Sparebank |
Sea1 Offshore |
Sogn Sparebank and Sea1 Offshore Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sogn Sparebank and Sea1 Offshore
The main advantage of trading using opposite Sogn Sparebank and Sea1 Offshore positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sogn Sparebank position performs unexpectedly, Sea1 Offshore 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 Sea1 Offshore will offset losses from the drop in Sea1 Offshore's long position.Sogn Sparebank vs. Holand og Setskog | Sogn Sparebank vs. Sparebank 1 Ringerike | Sogn Sparebank vs. SpareBank 1 stlandet |
Sea1 Offshore vs. Arcticzymes Technologies ASA | Sea1 Offshore vs. Xplora Technologies As | Sea1 Offshore vs. Romsdal Sparebank | Sea1 Offshore vs. Sogn Sparebank |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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