Correlation Between FAST RETAIL and China DatangRenewable
Can any of the company-specific risk be diversified away by investing in both FAST RETAIL and China DatangRenewable 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 FAST RETAIL and China DatangRenewable into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FAST RETAIL ADR and China Datang, you can compare the effects of market volatilities on FAST RETAIL and China DatangRenewable 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 FAST RETAIL with a short position of China DatangRenewable. Check out your portfolio center. Please also check ongoing floating volatility patterns of FAST RETAIL and China DatangRenewable.
Diversification Opportunities for FAST RETAIL and China DatangRenewable
0.1 | Correlation Coefficient |
Average diversification
The 3 months correlation between FAST and China is 0.1. Overlapping area represents the amount of risk that can be diversified away by holding FAST RETAIL ADR and China Datang in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China DatangRenewable and FAST RETAIL 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 FAST RETAIL ADR are associated (or correlated) with China DatangRenewable. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China DatangRenewable has no effect on the direction of FAST RETAIL i.e., FAST RETAIL and China DatangRenewable go up and down completely randomly.
Pair Corralation between FAST RETAIL and China DatangRenewable
Assuming the 90 days trading horizon FAST RETAIL ADR is expected to under-perform the China DatangRenewable. But the stock apears to be less risky and, when comparing its historical volatility, FAST RETAIL ADR is 1.37 times less risky than China DatangRenewable. The stock trades about -0.19 of its potential returns per unit of risk. The China Datang is currently generating about 0.01 of returns per unit of risk over similar time horizon. If you would invest 24.00 in China Datang on October 12, 2024 and sell it today you would earn a total of 0.00 from holding China Datang or generate 0.0% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
FAST RETAIL ADR vs. China Datang
Performance |
Timeline |
FAST RETAIL ADR |
China DatangRenewable |
FAST RETAIL and China DatangRenewable Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with FAST RETAIL and China DatangRenewable
The main advantage of trading using opposite FAST RETAIL and China DatangRenewable positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FAST RETAIL position performs unexpectedly, China DatangRenewable 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 DatangRenewable will offset losses from the drop in China DatangRenewable's long position.FAST RETAIL vs. BRAEMAR HOTELS RES | FAST RETAIL vs. Wyndham Hotels Resorts | FAST RETAIL vs. Xenia Hotels Resorts | FAST RETAIL vs. Nufarm Limited |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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