Correlation Between Seneca Foods and Borealis Foods
Can any of the company-specific risk be diversified away by investing in both Seneca Foods and Borealis Foods 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 Seneca Foods and Borealis Foods into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Seneca Foods Corp and Borealis Foods, you can compare the effects of market volatilities on Seneca Foods and Borealis Foods 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 Seneca Foods with a short position of Borealis Foods. Check out your portfolio center. Please also check ongoing floating volatility patterns of Seneca Foods and Borealis Foods.
Diversification Opportunities for Seneca Foods and Borealis Foods
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between Seneca and Borealis is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding Seneca Foods Corp and Borealis Foods in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Borealis Foods and Seneca Foods 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 Seneca Foods Corp are associated (or correlated) with Borealis Foods. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Borealis Foods has no effect on the direction of Seneca Foods i.e., Seneca Foods and Borealis Foods go up and down completely randomly.
Pair Corralation between Seneca Foods and Borealis Foods
Assuming the 90 days horizon Seneca Foods Corp is expected to generate 23.17 times more return on investment than Borealis Foods. However, Seneca Foods is 23.17 times more volatile than Borealis Foods. It trades about 0.18 of its potential returns per unit of risk. Borealis Foods is currently generating about 0.07 per unit of risk. If you would invest 5,921 in Seneca Foods Corp on September 3, 2024 and sell it today you would earn a total of 1,283 from holding Seneca Foods Corp or generate 21.67% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 50.0% |
| Values | Daily Returns |
Seneca Foods Corp vs. Borealis Foods
Performance |
| Timeline |
| Seneca Foods Corp |
| Borealis Foods |
Seneca Foods and Borealis Foods Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Seneca Foods and Borealis Foods
The main advantage of trading using opposite Seneca Foods and Borealis Foods positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Seneca Foods position performs unexpectedly, Borealis Foods 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 Borealis Foods will offset losses from the drop in Borealis Foods' long position.| Seneca Foods vs. Bridgford Foods | Seneca Foods vs. J J Snack | Seneca Foods vs. Central Garden Pet | Seneca Foods vs. Central Garden Pet |
| Borealis Foods vs. Seneca Foods Corp | Borealis Foods vs. J J Snack | Borealis Foods vs. Central Garden Pet | Borealis Foods vs. Central Garden Pet |
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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