Correlation Between REVO INSURANCE and Xinhua Winshare
Can any of the company-specific risk be diversified away by investing in both REVO INSURANCE and Xinhua Winshare 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 REVO INSURANCE and Xinhua Winshare into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between REVO INSURANCE SPA and Xinhua Winshare Publishing, you can compare the effects of market volatilities on REVO INSURANCE and Xinhua Winshare 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 REVO INSURANCE with a short position of Xinhua Winshare. Check out your portfolio center. Please also check ongoing floating volatility patterns of REVO INSURANCE and Xinhua Winshare.
Diversification Opportunities for REVO INSURANCE and Xinhua Winshare
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between REVO and Xinhua is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding REVO INSURANCE SPA and Xinhua Winshare Publishing in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Xinhua Winshare Publ and REVO INSURANCE 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 REVO INSURANCE SPA are associated (or correlated) with Xinhua Winshare. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Xinhua Winshare Publ has no effect on the direction of REVO INSURANCE i.e., REVO INSURANCE and Xinhua Winshare go up and down completely randomly.
Pair Corralation between REVO INSURANCE and Xinhua Winshare
Assuming the 90 days horizon REVO INSURANCE SPA is expected to under-perform the Xinhua Winshare. In addition to that, REVO INSURANCE is 1.2 times more volatile than Xinhua Winshare Publishing. It trades about -0.21 of its total potential returns per unit of risk. Xinhua Winshare Publishing is currently generating about -0.22 per unit of volatility. If you would invest 147.00 in Xinhua Winshare Publishing on November 3, 2024 and sell it today you would lose (15.00) from holding Xinhua Winshare Publishing or give up 10.2% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 95.65% |
Values | Daily Returns |
REVO INSURANCE SPA vs. Xinhua Winshare Publishing
Performance |
Timeline |
REVO INSURANCE SPA |
Xinhua Winshare Publ |
REVO INSURANCE and Xinhua Winshare Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with REVO INSURANCE and Xinhua Winshare
The main advantage of trading using opposite REVO INSURANCE and Xinhua Winshare positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if REVO INSURANCE position performs unexpectedly, Xinhua Winshare 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 Xinhua Winshare will offset losses from the drop in Xinhua Winshare's long position.REVO INSURANCE vs. BlueScope Steel Limited | REVO INSURANCE vs. Olympic Steel | REVO INSURANCE vs. WILLIS LEASE FIN | REVO INSURANCE vs. GRENKELEASING Dusseldorf |
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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 Correlation Analysis module to reduce portfolio risk simply by holding instruments which are not perfectly correlated.
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