Correlation Between BANK MANDIRI and VERTIV HOLCL
Can any of the company-specific risk be diversified away by investing in both BANK MANDIRI and VERTIV HOLCL 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 VERTIV HOLCL into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BANK MANDIRI and VERTIV HOLCL A, you can compare the effects of market volatilities on BANK MANDIRI and VERTIV HOLCL 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 VERTIV HOLCL. Check out your portfolio center. Please also check ongoing floating volatility patterns of BANK MANDIRI and VERTIV HOLCL.
Diversification Opportunities for BANK MANDIRI and VERTIV HOLCL
-0.54 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between BANK and VERTIV is -0.54. Overlapping area represents the amount of risk that can be diversified away by holding BANK MANDIRI and VERTIV HOLCL A in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on VERTIV HOLCL A 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 are associated (or correlated) with VERTIV HOLCL. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of VERTIV HOLCL A has no effect on the direction of BANK MANDIRI i.e., BANK MANDIRI and VERTIV HOLCL go up and down completely randomly.
Pair Corralation between BANK MANDIRI and VERTIV HOLCL
Assuming the 90 days trading horizon BANK MANDIRI is expected to generate 0.79 times more return on investment than VERTIV HOLCL. However, BANK MANDIRI is 1.26 times less risky than VERTIV HOLCL. It trades about -0.01 of its potential returns per unit of risk. VERTIV HOLCL A is currently generating about -0.02 per unit of risk. If you would invest 38.00 in BANK MANDIRI on October 30, 2024 and sell it today you would lose (2.00) from holding BANK MANDIRI or give up 5.26% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 95.0% |
| Values | Daily Returns |
BANK MANDIRI vs. VERTIV HOLCL A
Performance |
| Timeline |
| BANK MANDIRI |
| VERTIV HOLCL A |
BANK MANDIRI and VERTIV HOLCL Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with BANK MANDIRI and VERTIV HOLCL
The main advantage of trading using opposite BANK MANDIRI and VERTIV HOLCL positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BANK MANDIRI position performs unexpectedly, VERTIV HOLCL 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 VERTIV HOLCL will offset losses from the drop in VERTIV HOLCL's long position.| BANK MANDIRI vs. Yuexiu Transport Infrastructure | BANK MANDIRI vs. United Insurance Holdings | BANK MANDIRI vs. SIERRA METALS | BANK MANDIRI vs. Air Transport Services |
| VERTIV HOLCL vs. H2O Retailing | VERTIV HOLCL vs. RETAIL FOOD GROUP | VERTIV HOLCL vs. National Retail Properties | VERTIV HOLCL vs. Sumitomo Rubber Industries |
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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