Correlation Between CARSALESCOM and FedEx
Can any of the company-specific risk be diversified away by investing in both CARSALESCOM and FedEx 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 CARSALESCOM and FedEx into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CARSALESCOM and FedEx, you can compare the effects of market volatilities on CARSALESCOM and FedEx 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 CARSALESCOM with a short position of FedEx. Check out your portfolio center. Please also check ongoing floating volatility patterns of CARSALESCOM and FedEx.
Diversification Opportunities for CARSALESCOM and FedEx
0.76 | Correlation Coefficient |
Poor diversification
The 3 months correlation between CARSALESCOM and FedEx is 0.76. Overlapping area represents the amount of risk that can be diversified away by holding CARSALESCOM and FedEx in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FedEx and CARSALESCOM 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 CARSALESCOM are associated (or correlated) with FedEx. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FedEx has no effect on the direction of CARSALESCOM i.e., CARSALESCOM and FedEx go up and down completely randomly.
Pair Corralation between CARSALESCOM and FedEx
Assuming the 90 days trading horizon CARSALESCOM is expected to under-perform the FedEx. But the stock apears to be less risky and, when comparing its historical volatility, CARSALESCOM is 1.19 times less risky than FedEx. The stock trades about -0.05 of its potential returns per unit of risk. The FedEx is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest 23,810 in FedEx on September 29, 2024 and sell it today you would earn a total of 2,825 from holding FedEx or generate 11.86% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 98.41% |
Values | Daily Returns |
CARSALESCOM vs. FedEx
Performance |
Timeline |
CARSALESCOM |
FedEx |
CARSALESCOM and FedEx Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with CARSALESCOM and FedEx
The main advantage of trading using opposite CARSALESCOM and FedEx positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CARSALESCOM position performs unexpectedly, FedEx 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 FedEx will offset losses from the drop in FedEx's long position.The idea behind CARSALESCOM and FedEx 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.FedEx vs. United Parcel Service | FedEx vs. Deutsche Post AG | FedEx vs. DSV Panalpina AS | FedEx vs. ZTO Express |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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