Correlation Between American Eagle and SENECA FOODS-A
Can any of the company-specific risk be diversified away by investing in both American Eagle and SENECA FOODS-A 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 American Eagle and SENECA FOODS-A into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between American Eagle Outfitters and SENECA FOODS A, you can compare the effects of market volatilities on American Eagle and SENECA FOODS-A 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 American Eagle with a short position of SENECA FOODS-A. Check out your portfolio center. Please also check ongoing floating volatility patterns of American Eagle and SENECA FOODS-A.
Diversification Opportunities for American Eagle and SENECA FOODS-A
-0.33 | Correlation Coefficient |
Very good diversification
The 3 months correlation between American and SENECA is -0.33. Overlapping area represents the amount of risk that can be diversified away by holding American Eagle Outfitters and SENECA FOODS A in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SENECA FOODS A and American Eagle 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 American Eagle Outfitters are associated (or correlated) with SENECA FOODS-A. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SENECA FOODS A has no effect on the direction of American Eagle i.e., American Eagle and SENECA FOODS-A go up and down completely randomly.
Pair Corralation between American Eagle and SENECA FOODS-A
Assuming the 90 days trading horizon American Eagle is expected to generate 3.57 times less return on investment than SENECA FOODS-A. But when comparing it to its historical volatility, American Eagle Outfitters is 1.18 times less risky than SENECA FOODS-A. It trades about 0.05 of its potential returns per unit of risk. SENECA FOODS A is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 5,200 in SENECA FOODS A on September 4, 2024 and sell it today you would earn a total of 1,400 from holding SENECA FOODS A or generate 26.92% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.46% |
Values | Daily Returns |
American Eagle Outfitters vs. SENECA FOODS A
Performance |
Timeline |
American Eagle Outfitters |
SENECA FOODS A |
American Eagle and SENECA FOODS-A Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with American Eagle and SENECA FOODS-A
The main advantage of trading using opposite American Eagle and SENECA FOODS-A positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if American Eagle position performs unexpectedly, SENECA FOODS-A 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 SENECA FOODS-A will offset losses from the drop in SENECA FOODS-A's long position.American Eagle vs. Apple Inc | American Eagle vs. Apple Inc | American Eagle vs. Apple Inc | American Eagle vs. Apple Inc |
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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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