This module allows you to analyze existing cross correlation between Ford Motor Company and IPC. You can compare the effects of market volatilities on Ford Motor and IPC 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 IPC. See also your portfolio center. Please also check ongoing floating volatility patterns of Ford Motor and IPC.
|Horizon||30 Days Login to change|
Predicted Return Density
Ford Motor Company vs. IPC
Taking into account the 30 trading days horizon, Ford Motor Company is expected to under-perform the IPC. In addition to that, Ford Motor is 1.86 times more volatile than IPC. It trades about -0.12 of its total potential returns per unit of risk. IPC is currently generating about -0.22 per unit of volatility. If you would invest 4,374,372 in IPC on July 23, 2019 and sell it today you would lose (366,768) from holding IPC or give up 8.38% of portfolio value over 30 days.
Pair Corralation between Ford Motor and IPC
|Time Period||2 Months [change]|
Diversification Opportunities for Ford Motor and IPC
Very poor diversification
Overlapping area represents the amount of risk that can be diversified away by holding Ford Motor Company and IPC in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on IPC 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 IPC. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of IPC has no effect on the direction of Ford Motor i.e. Ford Motor and IPC go up and down completely randomly.
See also your portfolio center. Please also try Portfolio Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.