Correlation Between China Resources and AGL Energy
Can any of the company-specific risk be diversified away by investing in both China Resources and AGL Energy 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 AGL Energy 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 AGL Energy Limited, you can compare the effects of market volatilities on China Resources and AGL Energy 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 AGL Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of China Resources and AGL Energy.
Diversification Opportunities for China Resources and AGL Energy
-0.59 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between China and AGL is -0.59. Overlapping area represents the amount of risk that can be diversified away by holding China Resources Power and AGL Energy Limited in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AGL Energy Limited 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 AGL Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AGL Energy Limited has no effect on the direction of China Resources i.e., China Resources and AGL Energy go up and down completely randomly.
Pair Corralation between China Resources and AGL Energy
Assuming the 90 days horizon China Resources Power is expected to generate 2.12 times more return on investment than AGL Energy. However, China Resources is 2.12 times more volatile than AGL Energy Limited. It trades about 0.04 of its potential returns per unit of risk. AGL Energy Limited is currently generating about 0.06 per unit of risk. If you would invest 180.00 in China Resources Power on September 2, 2024 and sell it today you would earn a total of 45.00 from holding China Resources Power or generate 25.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 57.26% |
| Values | Daily Returns |
China Resources Power vs. AGL Energy Limited
Performance |
| Timeline |
| China Resources Power |
| AGL Energy Limited |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
China Resources and AGL Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with China Resources and AGL Energy
The main advantage of trading using opposite China Resources and AGL Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if China Resources position performs unexpectedly, AGL Energy 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 AGL Energy will offset losses from the drop in AGL Energy's long position.| China Resources vs. Maxim Power Corp | China Resources vs. Pampa Energia SA | China Resources vs. NRG Energy | China Resources vs. Vistra Energy Corp |
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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 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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