Correlation Between Bank Mandiri and China Southern
Can any of the company-specific risk be diversified away by investing in both Bank Mandiri and China Southern 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 Bank Mandiri and China Southern into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Mandiri Persero and China Southern Airlines, you can compare the effects of market volatilities on Bank Mandiri and China Southern 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 Bank Mandiri with a short position of China Southern. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Mandiri and China Southern.
Diversification Opportunities for Bank Mandiri and China Southern
-0.6 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Bank and China is -0.6. Overlapping area represents the amount of risk that can be diversified away by holding Bank Mandiri Persero and China Southern Airlines in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Southern Airlines and Bank Mandiri 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 Bank Mandiri Persero are associated (or correlated) with China Southern. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Southern Airlines has no effect on the direction of Bank Mandiri i.e., Bank Mandiri and China Southern go up and down completely randomly.
Pair Corralation between Bank Mandiri and China Southern
Assuming the 90 days horizon Bank Mandiri Persero is expected to under-perform the China Southern. In addition to that, Bank Mandiri is 1.87 times more volatile than China Southern Airlines. It trades about 0.0 of its total potential returns per unit of risk. China Southern Airlines is currently generating about 0.29 per unit of volatility. If you would invest 40.00 in China Southern Airlines on August 30, 2024 and sell it today you would earn a total of 6.00 from holding China Southern Airlines or generate 15.0% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 95.65% |
| Values | Daily Returns |
Bank Mandiri Persero vs. China Southern Airlines
Performance |
| Timeline |
| Bank Mandiri Persero |
| China Southern Airlines |
Bank Mandiri and China Southern Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank Mandiri and China Southern
The main advantage of trading using opposite Bank Mandiri and China Southern positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Mandiri position performs unexpectedly, China Southern 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 China Southern will offset losses from the drop in China Southern's long position.| Bank Mandiri vs. Israel Discount Bank | Bank Mandiri vs. Danske Bank AS | Bank Mandiri vs. Absa Group Limited |
| China Southern vs. Cebu Air | China Southern vs. Finnair Oyj | China Southern vs. easyJet plc | China Southern vs. Norse Atlantic ASA |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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