This module allows you to analyze existing cross correlation between Ford Motor Company and All Ords. You can compare the effects of market volatilities on Ford Motor and All Ords 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 Ford Motor with a short position of All Ords. See also your portfolio center. Please also check ongoing floating volatility patterns of Ford Motor and All Ords.
|Horizon||30 Days Login to change|
Ford Motor Company vs. All Ords
Taking into account the 30 trading days horizon, Ford Motor Company is expected to under-perform the All Ords. In addition to that, Ford Motor is 1.98 times more volatile than All Ords. It trades about 0.0 of its total potential returns per unit of risk. All Ords is currently generating about 0.1 per unit of volatility. If you would invest 659,810 in All Ords on June 20, 2019 and sell it today you would earn a total of 13,730 from holding All Ords or generate 2.08% return on investment over 30 days.
Pair Corralation between Ford Motor and All Ords
|Time Period||2 Months [change]|
Diversification Opportunities for Ford Motor and All Ords
Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor Company and All Ords in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on All Ords and Ford Motor 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 Ford Motor Company are associated (or correlated) with All Ords. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of All Ords has no effect on the direction of Ford Motor i.e. Ford Motor and All Ords go up and down completely randomly.
See also your portfolio center. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.