Correlation Between Planet Fitness and China Datang
Can any of the company-specific risk be diversified away by investing in both Planet Fitness and China Datang 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 Planet Fitness and China Datang into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Planet Fitness and China Datang, you can compare the effects of market volatilities on Planet Fitness and China Datang 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 Planet Fitness with a short position of China Datang. Check out your portfolio center. Please also check ongoing floating volatility patterns of Planet Fitness and China Datang.
Diversification Opportunities for Planet Fitness and China Datang
0.5 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Planet and China is 0.5. Overlapping area represents the amount of risk that can be diversified away by holding Planet Fitness and China Datang in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Datang and Planet Fitness 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 Planet Fitness are associated (or correlated) with China Datang. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Datang has no effect on the direction of Planet Fitness i.e., Planet Fitness and China Datang go up and down completely randomly.
Pair Corralation between Planet Fitness and China Datang
Assuming the 90 days horizon Planet Fitness is expected to generate 0.65 times more return on investment than China Datang. However, Planet Fitness is 1.54 times less risky than China Datang. It trades about 0.19 of its potential returns per unit of risk. China Datang is currently generating about 0.1 per unit of risk. If you would invest 9,550 in Planet Fitness on October 26, 2024 and sell it today you would earn a total of 550.00 from holding Planet Fitness or generate 5.76% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Planet Fitness vs. China Datang
Performance |
Timeline |
Planet Fitness |
China Datang |
Planet Fitness and China Datang Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Planet Fitness and China Datang
The main advantage of trading using opposite Planet Fitness and China Datang positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Planet Fitness position performs unexpectedly, China Datang 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 China Datang will offset losses from the drop in China Datang's long position.Planet Fitness vs. PULSION Medical Systems | Planet Fitness vs. SCANDMEDICAL SOLDK 040 | Planet Fitness vs. Tradegate AG Wertpapierhandelsbank | Planet Fitness vs. CANON MARKETING JP |
China Datang vs. NextEra Energy | China Datang vs. The Southern | China Datang vs. VERBUND AG | China Datang vs. PGE Corporation |
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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