This module allows you to analyze existing cross correlation between ATX and Straits Tms. You can compare the effects of market volatilities on ATX and Straits Tms 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 ATX with a short position of Straits Tms. See also your portfolio center. Please also check ongoing floating volatility patterns of ATX and Straits Tms.
|Horizon||30 Days Login to change|
Predicted Return Density
ATX vs. Straits Tms
Given the investment horizon of 30 days, ATX is expected to under-perform the Straits Tms. In addition to that, ATX is 1.05 times more volatile than Straits Tms. It trades about -0.43 of its total potential returns per unit of risk. Straits Tms is currently generating about -0.16 per unit of volatility. If you would invest 335,770 in Straits Tms on May 19, 2019 and sell it today you would lose (14,971) from holding Straits Tms or give up 4.46% of portfolio value over 30 days.
Pair Corralation between ATX and Straits Tms
|Time Period||2 Months [change]|
Diversification Opportunities for ATX and Straits Tms
Almost no diversification
Overlapping area represents the amount of risk that can be diversified away by holding ATX and Straits Tms in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on Straits Tms and ATX 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 ATX are associated (or correlated) with Straits Tms. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Straits Tms has no effect on the direction of ATX i.e. ATX and Straits Tms go up and down completely randomly.
See also your portfolio center. Please also try Headlines Timeline module to stay connected to all market stories and filter out noise. drill down to analyze hype elasticity.