Correlation Between Bank Rakyat and PT Multi
Can any of the company-specific risk be diversified away by investing in both Bank Rakyat and PT Multi 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 Rakyat and PT Multi into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bank Rakyat Indonesia and PT Multi Garam, you can compare the effects of market volatilities on Bank Rakyat and PT Multi 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 Rakyat with a short position of PT Multi. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bank Rakyat and PT Multi.
Diversification Opportunities for Bank Rakyat and PT Multi
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Bank and FOLK is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding Bank Rakyat Indonesia and PT Multi Garam in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PT Multi Garam and Bank Rakyat 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 Rakyat Indonesia are associated (or correlated) with PT Multi. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PT Multi Garam has no effect on the direction of Bank Rakyat i.e., Bank Rakyat and PT Multi go up and down completely randomly.
Pair Corralation between Bank Rakyat and PT Multi
Assuming the 90 days trading horizon Bank Rakyat Indonesia is expected to under-perform the PT Multi. But the stock apears to be less risky and, when comparing its historical volatility, Bank Rakyat Indonesia is 2.42 times less risky than PT Multi. The stock trades about -0.05 of its potential returns per unit of risk. The PT Multi Garam is currently generating about 0.0 of returns per unit of risk over similar time horizon. If you would invest 5,400 in PT Multi Garam on October 7, 2024 and sell it today you would lose (400.00) from holding PT Multi Garam or give up 7.41% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bank Rakyat Indonesia vs. PT Multi Garam
Performance |
Timeline |
Bank Rakyat Indonesia |
PT Multi Garam |
Bank Rakyat and PT Multi Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bank Rakyat and PT Multi
The main advantage of trading using opposite Bank Rakyat and PT Multi positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bank Rakyat position performs unexpectedly, PT Multi 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 PT Multi will offset losses from the drop in PT Multi's long position.Bank Rakyat vs. Bank Central Asia | Bank Rakyat vs. Bank Mandiri Persero | Bank Rakyat vs. Bank Negara Indonesia | Bank Rakyat vs. Telkom Indonesia Tbk |
PT Multi vs. Panin Financial Tbk | PT Multi vs. Media Nusantara Citra | PT Multi vs. PT Wahana Interfood | PT Multi vs. Global Mediacom Tbk |
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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