Correlation Between Xinhua Winshare and Food Life
Can any of the company-specific risk be diversified away by investing in both Xinhua Winshare and Food Life 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 Xinhua Winshare and Food Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Xinhua Winshare Publishing and Food Life Companies, you can compare the effects of market volatilities on Xinhua Winshare and Food Life 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 Xinhua Winshare with a short position of Food Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of Xinhua Winshare and Food Life.
Diversification Opportunities for Xinhua Winshare and Food Life
0.66 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Xinhua and Food is 0.66. Overlapping area represents the amount of risk that can be diversified away by holding Xinhua Winshare Publishing and Food Life Companies in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Food Life Companies and Xinhua Winshare 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 Xinhua Winshare Publishing are associated (or correlated) with Food Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Food Life Companies has no effect on the direction of Xinhua Winshare i.e., Xinhua Winshare and Food Life go up and down completely randomly.
Pair Corralation between Xinhua Winshare and Food Life
Assuming the 90 days horizon Xinhua Winshare Publishing is expected to generate 1.4 times more return on investment than Food Life. However, Xinhua Winshare is 1.4 times more volatile than Food Life Companies. It trades about 0.38 of its potential returns per unit of risk. Food Life Companies is currently generating about -0.18 per unit of risk. If you would invest 123.00 in Xinhua Winshare Publishing on October 8, 2024 and sell it today you would earn a total of 17.00 from holding Xinhua Winshare Publishing or generate 13.82% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Xinhua Winshare Publishing vs. Food Life Companies
Performance |
Timeline |
Xinhua Winshare Publ |
Food Life Companies |
Xinhua Winshare and Food Life Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Xinhua Winshare and Food Life
The main advantage of trading using opposite Xinhua Winshare and Food Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Xinhua Winshare position performs unexpectedly, Food Life 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 Food Life will offset losses from the drop in Food Life's long position.Xinhua Winshare vs. Pearson plc | Xinhua Winshare vs. Superior Plus Corp | Xinhua Winshare vs. NMI Holdings | Xinhua Winshare vs. SIVERS SEMICONDUCTORS AB |
Food Life vs. Superior Plus Corp | Food Life vs. NMI Holdings | Food Life vs. SIVERS SEMICONDUCTORS AB | Food Life vs. Talanx AG |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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