This module allows you to analyze existing cross correlation between Foot Locker and BSE. You can compare the effects of market volatilities on Foot Locker and BSE 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 Foot Locker with a short position of BSE. See also your portfolio center. Please also check ongoing floating volatility patterns of Foot Locker and BSE.
|Horizon||30 Days Login to change|
Predicted Return Density
Foot Locker Inc vs. BSE
Allowing for the 30-days total investment horizon, Foot Locker is expected to under-perform the BSE. In addition to that, Foot Locker is 2.64 times more volatile than BSE. It trades about -0.08 of its total potential returns per unit of risk. BSE is currently generating about -0.14 per unit of volatility. If you would invest 3,912,296 in BSE on July 22, 2019 and sell it today you would lose (188,812) from holding BSE or give up 4.83% of portfolio value over 30 days.
Pair Corralation between Foot Locker and BSE
|Time Period||2 Months [change]|
Diversification Opportunities for Foot Locker and BSE
Very weak diversification
Overlapping area represents the amount of risk that can be diversified away by holding Foot Locker Inc and BSE in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on BSE and Foot Locker 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 Foot Locker are associated (or correlated) with BSE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BSE has no effect on the direction of Foot Locker i.e. Foot Locker and BSE go up and down completely randomly.
See also your portfolio center. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.