Correlation Between Great China and Power Wind
Can any of the company-specific risk be diversified away by investing in both Great China and Power Wind 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 Great China and Power Wind into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Great China Metal and Power Wind Health, you can compare the effects of market volatilities on Great China and Power Wind 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 Great China with a short position of Power Wind. Check out your portfolio center. Please also check ongoing floating volatility patterns of Great China and Power Wind.
Diversification Opportunities for Great China and Power Wind
-0.08 | Correlation Coefficient |
Good diversification
The 3 months correlation between Great and Power is -0.08. Overlapping area represents the amount of risk that can be diversified away by holding Great China Metal and Power Wind Health in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Power Wind Health and Great China 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 Great China Metal are associated (or correlated) with Power Wind. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Power Wind Health has no effect on the direction of Great China i.e., Great China and Power Wind go up and down completely randomly.
Pair Corralation between Great China and Power Wind
Assuming the 90 days trading horizon Great China Metal is expected to generate 0.57 times more return on investment than Power Wind. However, Great China Metal is 1.75 times less risky than Power Wind. It trades about 0.0 of its potential returns per unit of risk. Power Wind Health is currently generating about -0.12 per unit of risk. If you would invest 2,290 in Great China Metal on September 2, 2024 and sell it today you would earn a total of 0.00 from holding Great China Metal or generate 0.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Great China Metal vs. Power Wind Health
Performance |
| Timeline |
| Great China Metal |
| Power Wind Health |
Great China and Power Wind Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Great China and Power Wind
The main advantage of trading using opposite Great China and Power Wind positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Great China position performs unexpectedly, Power Wind 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 Power Wind will offset losses from the drop in Power Wind's long position.| Great China vs. Basso Industry Corp | Great China vs. Chung Hsin Electric Machinery | Great China vs. TYC Brother Industrial | Great China vs. TECO Electric Machinery |
| Power Wind vs. Sunny Friend Environmental | Power Wind vs. Cleanaway Co | Power Wind vs. Charoen Pokphand Enterprise | Power Wind vs. TTET Union Corp |
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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