Correlation Between Algorand and BANK CIMB
Can any of the company-specific risk be diversified away by investing in both Algorand and BANK CIMB 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 Algorand and BANK CIMB into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Algorand and BANK CIMB NIAGA, you can compare the effects of market volatilities on Algorand and BANK CIMB 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 Algorand with a short position of BANK CIMB. Check out your portfolio center. Please also check ongoing floating volatility patterns of Algorand and BANK CIMB.
Diversification Opportunities for Algorand and BANK CIMB
Very poor diversification
The 3 months correlation between Algorand and BANK is 0.85. Overlapping area represents the amount of risk that can be diversified away by holding Algorand and BANK CIMB NIAGA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BANK CIMB NIAGA and Algorand 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 Algorand are associated (or correlated) with BANK CIMB. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BANK CIMB NIAGA has no effect on the direction of Algorand i.e., Algorand and BANK CIMB go up and down completely randomly.
Pair Corralation between Algorand and BANK CIMB
Assuming the 90 days trading horizon Algorand is expected to under-perform the BANK CIMB. In addition to that, Algorand is 3.77 times more volatile than BANK CIMB NIAGA. It trades about -0.15 of its total potential returns per unit of risk. BANK CIMB NIAGA is currently generating about -0.11 per unit of volatility. If you would invest 9.35 in BANK CIMB NIAGA on December 22, 2024 and sell it today you would lose (1.00) from holding BANK CIMB NIAGA or give up 10.7% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 92.19% |
Values | Daily Returns |
Algorand vs. BANK CIMB NIAGA
Performance |
Timeline |
Algorand |
BANK CIMB NIAGA |
Algorand and BANK CIMB Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Algorand and BANK CIMB
The main advantage of trading using opposite Algorand and BANK CIMB positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Algorand position performs unexpectedly, BANK CIMB 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 CIMB will offset losses from the drop in BANK CIMB's long position.The idea behind Algorand and BANK CIMB NIAGA pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.BANK CIMB vs. Osisko Metals | BANK CIMB vs. Q2M Managementberatung AG | BANK CIMB vs. GREENX METALS LTD | BANK CIMB vs. Perdoceo Education |
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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