Correlation Between Wenzhou Kangning and PICKN PAY
Can any of the company-specific risk be diversified away by investing in both Wenzhou Kangning and PICKN PAY 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 Wenzhou Kangning and PICKN PAY into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Wenzhou Kangning Hospital and PICKN PAY STORES, you can compare the effects of market volatilities on Wenzhou Kangning and PICKN PAY 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 Wenzhou Kangning with a short position of PICKN PAY. Check out your portfolio center. Please also check ongoing floating volatility patterns of Wenzhou Kangning and PICKN PAY.
Diversification Opportunities for Wenzhou Kangning and PICKN PAY
0.67 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Wenzhou and PICKN is 0.67. Overlapping area represents the amount of risk that can be diversified away by holding Wenzhou Kangning Hospital and PICKN PAY STORES in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PICKN PAY STORES and Wenzhou Kangning 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 Wenzhou Kangning Hospital are associated (or correlated) with PICKN PAY. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PICKN PAY STORES has no effect on the direction of Wenzhou Kangning i.e., Wenzhou Kangning and PICKN PAY go up and down completely randomly.
Pair Corralation between Wenzhou Kangning and PICKN PAY
Assuming the 90 days trading horizon Wenzhou Kangning Hospital is expected to under-perform the PICKN PAY. But the stock apears to be less risky and, when comparing its historical volatility, Wenzhou Kangning Hospital is 1.02 times less risky than PICKN PAY. The stock trades about -0.24 of its potential returns per unit of risk. The PICKN PAY STORES is currently generating about -0.06 of returns per unit of risk over similar time horizon. If you would invest 154.00 in PICKN PAY STORES on December 22, 2024 and sell it today you would lose (17.00) from holding PICKN PAY STORES or give up 11.04% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Wenzhou Kangning Hospital vs. PICKN PAY STORES
Performance |
Timeline |
Wenzhou Kangning Hospital |
PICKN PAY STORES |
Wenzhou Kangning and PICKN PAY Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Wenzhou Kangning and PICKN PAY
The main advantage of trading using opposite Wenzhou Kangning and PICKN PAY positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Wenzhou Kangning position performs unexpectedly, PICKN PAY 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 PICKN PAY will offset losses from the drop in PICKN PAY's long position.Wenzhou Kangning vs. ETFS Coffee ETC | Wenzhou Kangning vs. SBM OFFSHORE | Wenzhou Kangning vs. BJs Restaurants | Wenzhou Kangning vs. TYSON FOODS A |
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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 Equity Valuation module to check real value of public entities based on technical and fundamental data.
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