Correlation Between China Resources and Power Assets
Can any of the company-specific risk be diversified away by investing in both China Resources and Power Assets 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 China Resources and Power Assets into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between China Resources Power and Power Assets Holdings, you can compare the effects of market volatilities on China Resources and Power Assets 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 China Resources with a short position of Power Assets. Check out your portfolio center. Please also check ongoing floating volatility patterns of China Resources and Power Assets.
Diversification Opportunities for China Resources and Power Assets
0.12 | Correlation Coefficient |
Average diversification
The 3 months correlation between China and Power is 0.12. Overlapping area represents the amount of risk that can be diversified away by holding China Resources Power and Power Assets Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Power Assets Holdings and China Resources 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 China Resources Power are associated (or correlated) with Power Assets. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Power Assets Holdings has no effect on the direction of China Resources i.e., China Resources and Power Assets go up and down completely randomly.
Pair Corralation between China Resources and Power Assets
Assuming the 90 days horizon China Resources Power is expected to under-perform the Power Assets. In addition to that, China Resources is 1.37 times more volatile than Power Assets Holdings. It trades about -0.07 of its total potential returns per unit of risk. Power Assets Holdings is currently generating about -0.04 per unit of volatility. If you would invest 682.00 in Power Assets Holdings on September 22, 2024 and sell it today you would lose (29.00) from holding Power Assets Holdings or give up 4.25% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
China Resources Power vs. Power Assets Holdings
Performance |
Timeline |
China Resources Power |
Power Assets Holdings |
China Resources and Power Assets Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with China Resources and Power Assets
The main advantage of trading using opposite China Resources and Power Assets positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if China Resources position performs unexpectedly, Power Assets 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 Assets will offset losses from the drop in Power Assets' long position.China Resources vs. Kenon Holdings | China Resources vs. NRG Energy | China Resources vs. Centrica plc | China Resources vs. Vistra Energy Corp |
Power Assets vs. Energy of Minas | Power Assets vs. Avista | Power Assets vs. Allete Inc | Power Assets vs. The AES |
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 Fundamental Analysis module to view fundamental data based on most recent published financial statements.
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