Correlation Between Deutsche Telekom and Fanhua
Can any of the company-specific risk be diversified away by investing in both Deutsche Telekom and Fanhua 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 Fanhua 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 Fanhua Inc, you can compare the effects of market volatilities on Deutsche Telekom and Fanhua 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 Fanhua. Check out your portfolio center. Please also check ongoing floating volatility patterns of Deutsche Telekom and Fanhua.
Diversification Opportunities for Deutsche Telekom and Fanhua
-0.35 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Deutsche and Fanhua is -0.35. Overlapping area represents the amount of risk that can be diversified away by holding Deutsche Telekom AG and Fanhua Inc in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fanhua Inc 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 Fanhua. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fanhua Inc has no effect on the direction of Deutsche Telekom i.e., Deutsche Telekom and Fanhua go up and down completely randomly.
Pair Corralation between Deutsche Telekom and Fanhua
Assuming the 90 days trading horizon Deutsche Telekom AG is expected to generate 0.17 times more return on investment than Fanhua. However, Deutsche Telekom AG is 5.8 times less risky than Fanhua. It trades about 0.1 of its potential returns per unit of risk. Fanhua Inc is currently generating about -0.08 per unit of risk. If you would invest 1,886 in Deutsche Telekom AG on October 26, 2024 and sell it today you would earn a total of 1,049 from holding Deutsche Telekom AG or generate 55.62% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Deutsche Telekom AG vs. Fanhua Inc
Performance |
| Timeline |
| Deutsche Telekom |
| Fanhua Inc |
Deutsche Telekom and Fanhua Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Deutsche Telekom and Fanhua
The main advantage of trading using opposite Deutsche Telekom and Fanhua positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Deutsche Telekom position performs unexpectedly, Fanhua 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 Fanhua will offset losses from the drop in Fanhua's long position.| Deutsche Telekom vs. National Beverage Corp | Deutsche Telekom vs. CAL MAINE FOODS | Deutsche Telekom vs. Gruppo Mutuionline SpA | Deutsche Telekom vs. Thai Beverage Public |
| Fanhua vs. Westinghouse Air Brake | Fanhua vs. RYANAIR HLDGS ADR | Fanhua vs. CHINA SOUTHN AIR H | Fanhua vs. EAGLE MATERIALS |
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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