Correlation Between Bank Mnc and Bank Artha
Can any of the company-specific risk be diversified away by investing in both Bank Mnc and Bank Artha 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 Mnc and Bank Artha into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Mnc Internasional and Bank Artha Graha, you can compare the effects of market volatilities on Bank Mnc and Bank Artha 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 Mnc with a short position of Bank Artha. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Mnc and Bank Artha.
Diversification Opportunities for Bank Mnc and Bank Artha
-0.61 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Bank and Bank is -0.61. Overlapping area represents the amount of risk that can be diversified away by holding Bank Mnc Internasional and Bank Artha Graha in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bank Artha Graha and Bank Mnc 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 Mnc Internasional are associated (or correlated) with Bank Artha. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bank Artha Graha has no effect on the direction of Bank Mnc i.e., Bank Mnc and Bank Artha go up and down completely randomly.
Pair Corralation between Bank Mnc and Bank Artha
Assuming the 90 days trading horizon Bank Mnc Internasional is expected to under-perform the Bank Artha. But the stock apears to be less risky and, when comparing its historical volatility, Bank Mnc Internasional is 4.14 times less risky than Bank Artha. The stock trades about -0.19 of its potential returns per unit of risk. The Bank Artha Graha is currently generating about 0.35 of returns per unit of risk over similar time horizon. If you would invest 15,700 in Bank Artha Graha on August 31, 2024 and sell it today you would earn a total of 20,300 from holding Bank Artha Graha or generate 129.3% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Bank Mnc Internasional vs. Bank Artha Graha
Performance |
| Timeline |
| Bank Mnc Internasional |
| Bank Artha Graha |
Bank Mnc and Bank Artha Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Bank Mnc and Bank Artha
The main advantage of trading using opposite Bank Mnc and Bank Artha positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Mnc position performs unexpectedly, Bank Artha 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 Bank Artha will offset losses from the drop in Bank Artha's long position.| Bank Mnc vs. Bank Capital Indonesia | Bank Mnc vs. Bank Pembangunan Daerah | Bank Mnc vs. Bank Victoria International | Bank Mnc vs. Bank Qnb Indonesia |
| Bank Artha vs. Bank Victoria International | Bank Artha vs. Bank Bumi Arta | Bank Artha vs. Bank Mnc Internasional | Bank Artha vs. Bank Qnb Indonesia |
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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