Correlation Between Bank Mandiri and Wireless Telecom
Can any of the company-specific risk be diversified away by investing in both Bank Mandiri and Wireless Telecom 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 Wireless Telecom 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 Wireless Telecom Group, you can compare the effects of market volatilities on Bank Mandiri and Wireless Telecom 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 Wireless Telecom. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Mandiri and Wireless Telecom.
Diversification Opportunities for Bank Mandiri and Wireless Telecom
-0.71 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Bank and Wireless is -0.71. Overlapping area represents the amount of risk that can be diversified away by holding Bank Mandiri Persero and Wireless Telecom Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wireless Telecom 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 Wireless Telecom. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wireless Telecom has no effect on the direction of Bank Mandiri i.e., Bank Mandiri and Wireless Telecom go up and down completely randomly.
Pair Corralation between Bank Mandiri and Wireless Telecom
Assuming the 90 days horizon Bank Mandiri is expected to generate 2.81 times less return on investment than Wireless Telecom. But when comparing it to its historical volatility, Bank Mandiri Persero is 1.69 times less risky than Wireless Telecom. It trades about 0.04 of its potential returns per unit of risk. Wireless Telecom Group is currently generating about 0.06 of returns per unit of risk over similar time horizon. If you would invest 168.00 in Wireless Telecom Group on September 2, 2024 and sell it today you would earn a total of 43.00 from holding Wireless Telecom Group or generate 25.6% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 30.85% |
Values | Daily Returns |
Bank Mandiri Persero vs. Wireless Telecom Group
Performance |
Timeline |
Bank Mandiri Persero |
Wireless Telecom |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Bank Mandiri and Wireless Telecom Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Mandiri and Wireless Telecom
The main advantage of trading using opposite Bank Mandiri and Wireless Telecom positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Mandiri position performs unexpectedly, Wireless Telecom 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 Wireless Telecom will offset losses from the drop in Wireless Telecom's long position.Bank Mandiri vs. Piraeus Bank SA | Bank Mandiri vs. Turkiye Garanti Bankasi | Bank Mandiri vs. Uwharrie Capital 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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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