Correlation Between NISSIN FOODS and Performance Food
Can any of the company-specific risk be diversified away by investing in both NISSIN FOODS and Performance Food 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 NISSIN FOODS and Performance Food into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between NISSIN FOODS HLDGS and Performance Food Group, you can compare the effects of market volatilities on NISSIN FOODS and Performance Food 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 NISSIN FOODS with a short position of Performance Food. Check out your portfolio center. Please also check ongoing floating volatility patterns of NISSIN FOODS and Performance Food.
Diversification Opportunities for NISSIN FOODS and Performance Food
-0.01 | Correlation Coefficient |
Good diversification
The 3 months correlation between NISSIN and Performance is -0.01. Overlapping area represents the amount of risk that can be diversified away by holding NISSIN FOODS HLDGS and Performance Food Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Performance Food and NISSIN 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 NISSIN FOODS HLDGS are associated (or correlated) with Performance Food. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Performance Food has no effect on the direction of NISSIN FOODS i.e., NISSIN FOODS and Performance Food go up and down completely randomly.
Pair Corralation between NISSIN FOODS and Performance Food
Assuming the 90 days trading horizon NISSIN FOODS HLDGS is expected to under-perform the Performance Food. In addition to that, NISSIN FOODS is 1.04 times more volatile than Performance Food Group. It trades about -0.03 of its total potential returns per unit of risk. Performance Food Group is currently generating about 0.06 per unit of volatility. If you would invest 5,350 in Performance Food Group on November 20, 2024 and sell it today you would earn a total of 2,900 from holding Performance Food Group or generate 54.21% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
NISSIN FOODS HLDGS vs. Performance Food Group
Performance |
Timeline |
NISSIN FOODS HLDGS |
Performance Food |
NISSIN FOODS and Performance Food Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with NISSIN FOODS and Performance Food
The main advantage of trading using opposite NISSIN FOODS and Performance Food positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if NISSIN FOODS position performs unexpectedly, Performance Food 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 Performance Food will offset losses from the drop in Performance Food's long position.NISSIN FOODS vs. FORTRESS BIOTECHPRFA 25 | NISSIN FOODS vs. Allegheny Technologies Incorporated | NISSIN FOODS vs. Tower Semiconductor | NISSIN FOODS vs. Lendlease Group |
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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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