Correlation Between Sparebank and Sunndal Sparebank
Can any of the company-specific risk be diversified away by investing in both Sparebank and Sunndal Sparebank 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 Sparebank and Sunndal Sparebank into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sparebank 1 Ostfold and Sunndal Sparebank, you can compare the effects of market volatilities on Sparebank and Sunndal Sparebank 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 Sparebank with a short position of Sunndal Sparebank. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sparebank and Sunndal Sparebank.
Diversification Opportunities for Sparebank and Sunndal Sparebank
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Sparebank and Sunndal is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding Sparebank 1 Ostfold and Sunndal Sparebank in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sunndal Sparebank and 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 Sparebank 1 Ostfold are associated (or correlated) with Sunndal Sparebank. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sunndal Sparebank has no effect on the direction of Sparebank i.e., Sparebank and Sunndal Sparebank go up and down completely randomly.
Pair Corralation between Sparebank and Sunndal Sparebank
Assuming the 90 days trading horizon Sparebank 1 Ostfold is expected to generate 1.04 times more return on investment than Sunndal Sparebank. However, Sparebank is 1.04 times more volatile than Sunndal Sparebank. It trades about -0.06 of its potential returns per unit of risk. Sunndal Sparebank is currently generating about -0.17 per unit of risk. If you would invest 38,000 in Sparebank 1 Ostfold on September 1, 2024 and sell it today you would lose (500.00) from holding Sparebank 1 Ostfold or give up 1.32% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 95.65% |
| Values | Daily Returns |
Sparebank 1 Ostfold vs. Sunndal Sparebank
Performance |
| Timeline |
| Sparebank 1 Ostfold |
| Sunndal Sparebank |
Sparebank and Sunndal Sparebank Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Sparebank and Sunndal Sparebank
The main advantage of trading using opposite Sparebank and Sunndal Sparebank positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sparebank position performs unexpectedly, Sunndal Sparebank 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 Sunndal Sparebank will offset losses from the drop in Sunndal Sparebank's long position.| Sparebank vs. DnB ASA | Sparebank vs. Gjensidige Forsikring ASA | Sparebank vs. Orkla ASA | Sparebank vs. Telenor ASA |
| Sunndal Sparebank vs. Proximar Seafood AS | Sunndal Sparebank vs. Polaris Media | Sunndal Sparebank vs. 5Th Planet Games | Sunndal Sparebank vs. Shelf Drilling |
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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