Correlation Between Deutsche Telekom and GANGLONG CHINA
Can any of the company-specific risk be diversified away by investing in both Deutsche Telekom and GANGLONG CHINA 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 Deutsche Telekom and GANGLONG CHINA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Deutsche Telekom AG and GANGLONG CHINA PRGRLTD, you can compare the effects of market volatilities on Deutsche Telekom and GANGLONG CHINA 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 Deutsche Telekom with a short position of GANGLONG CHINA. Check out your portfolio center. Please also check ongoing floating volatility patterns of Deutsche Telekom and GANGLONG CHINA.
Diversification Opportunities for Deutsche Telekom and GANGLONG CHINA
-0.33 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Deutsche and GANGLONG is -0.33. Overlapping area represents the amount of risk that can be diversified away by holding Deutsche Telekom AG and GANGLONG CHINA PRGRLTD in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on GANGLONG CHINA PRGRLTD and Deutsche Telekom 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 Deutsche Telekom AG are associated (or correlated) with GANGLONG CHINA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of GANGLONG CHINA PRGRLTD has no effect on the direction of Deutsche Telekom i.e., Deutsche Telekom and GANGLONG CHINA go up and down completely randomly.
Pair Corralation between Deutsche Telekom and GANGLONG CHINA
Assuming the 90 days trading horizon Deutsche Telekom is expected to generate 48.05 times less return on investment than GANGLONG CHINA. But when comparing it to its historical volatility, Deutsche Telekom AG is 79.79 times less risky than GANGLONG CHINA. It trades about 0.27 of its potential returns per unit of risk. GANGLONG CHINA PRGRLTD is currently generating about 0.16 of returns per unit of risk over similar time horizon. If you would invest 0.85 in GANGLONG CHINA PRGRLTD on October 20, 2024 and sell it today you would lose (0.05) from holding GANGLONG CHINA PRGRLTD or give up 5.88% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Deutsche Telekom AG vs. GANGLONG CHINA PRGRLTD
Performance |
Timeline |
Deutsche Telekom |
GANGLONG CHINA PRGRLTD |
Deutsche Telekom and GANGLONG CHINA Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Deutsche Telekom and GANGLONG CHINA
The main advantage of trading using opposite Deutsche Telekom and GANGLONG CHINA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Deutsche Telekom position performs unexpectedly, GANGLONG CHINA 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 GANGLONG CHINA will offset losses from the drop in GANGLONG CHINA's long position.Deutsche Telekom vs. MOUNT GIBSON IRON | Deutsche Telekom vs. H2O Retailing | Deutsche Telekom vs. ANGANG STEEL H | Deutsche Telekom vs. CALTAGIRONE EDITORE |
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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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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