Correlation Between China Mobile and AVer Information
Can any of the company-specific risk be diversified away by investing in both China Mobile and AVer Information 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 Mobile and AVer Information into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between China Mobile and AVer Information, you can compare the effects of market volatilities on China Mobile and AVer Information 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 Mobile with a short position of AVer Information. Check out your portfolio center. Please also check ongoing floating volatility patterns of China Mobile and AVer Information.
Diversification Opportunities for China Mobile and AVer Information
0.31 | Correlation Coefficient |
Weak diversification
The 3 months correlation between China and AVer is 0.31. Overlapping area represents the amount of risk that can be diversified away by holding China Mobile and AVer Information in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on AVer Information and China Mobile 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 Mobile are associated (or correlated) with AVer Information. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of AVer Information has no effect on the direction of China Mobile i.e., China Mobile and AVer Information go up and down completely randomly.
Pair Corralation between China Mobile and AVer Information
Assuming the 90 days trading horizon China Mobile is expected to generate 0.69 times more return on investment than AVer Information. However, China Mobile is 1.45 times less risky than AVer Information. It trades about -0.02 of its potential returns per unit of risk. AVer Information is currently generating about -0.04 per unit of risk. If you would invest 1,365 in China Mobile on October 16, 2024 and sell it today you would lose (9.00) from holding China Mobile or give up 0.66% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 95.24% |
| Values | Daily Returns |
China Mobile vs. AVer Information
Performance |
| Timeline |
| China Mobile |
| AVer Information |
China Mobile and AVer Information Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with China Mobile and AVer Information
The main advantage of trading using opposite China Mobile and AVer Information positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if China Mobile position performs unexpectedly, AVer Information 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 AVer Information will offset losses from the drop in AVer Information's long position.| China Mobile vs. An Shin Food Services | China Mobile vs. Huang Hsiang Construction | China Mobile vs. Strong H Machinery | China Mobile vs. Highwealth Construction Corp |
| AVer Information vs. O Bank Co | AVer Information vs. Softstar Entertainment | AVer Information vs. Shinkong Insurance Co | AVer Information vs. Taichung Commercial Bank |
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 Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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