Correlation Between Fast Retailing and COMMERCIAL VEHICLE
Can any of the company-specific risk be diversified away by investing in both Fast Retailing and COMMERCIAL VEHICLE 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 Fast Retailing and COMMERCIAL VEHICLE into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Fast Retailing Co and COMMERCIAL VEHICLE, you can compare the effects of market volatilities on Fast Retailing and COMMERCIAL VEHICLE 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 Fast Retailing with a short position of COMMERCIAL VEHICLE. Check out your portfolio center. Please also check ongoing floating volatility patterns of Fast Retailing and COMMERCIAL VEHICLE.
Diversification Opportunities for Fast Retailing and COMMERCIAL VEHICLE
0.72 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Fast and COMMERCIAL is 0.72. Overlapping area represents the amount of risk that can be diversified away by holding Fast Retailing Co and COMMERCIAL VEHICLE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on COMMERCIAL VEHICLE and Fast Retailing 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 Fast Retailing Co are associated (or correlated) with COMMERCIAL VEHICLE. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of COMMERCIAL VEHICLE has no effect on the direction of Fast Retailing i.e., Fast Retailing and COMMERCIAL VEHICLE go up and down completely randomly.
Pair Corralation between Fast Retailing and COMMERCIAL VEHICLE
Assuming the 90 days trading horizon Fast Retailing Co is expected to generate 0.48 times more return on investment than COMMERCIAL VEHICLE. However, Fast Retailing Co is 2.09 times less risky than COMMERCIAL VEHICLE. It trades about -0.11 of its potential returns per unit of risk. COMMERCIAL VEHICLE is currently generating about -0.09 per unit of risk. If you would invest 33,424 in Fast Retailing Co on December 5, 2024 and sell it today you would lose (4,194) from holding Fast Retailing Co or give up 12.55% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Fast Retailing Co vs. COMMERCIAL VEHICLE
Performance |
Timeline |
Fast Retailing |
COMMERCIAL VEHICLE |
Fast Retailing and COMMERCIAL VEHICLE Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Fast Retailing and COMMERCIAL VEHICLE
The main advantage of trading using opposite Fast Retailing and COMMERCIAL VEHICLE positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Fast Retailing position performs unexpectedly, COMMERCIAL VEHICLE 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 COMMERCIAL VEHICLE will offset losses from the drop in COMMERCIAL VEHICLE's long position.Fast Retailing vs. Tower One Wireless | Fast Retailing vs. Iridium Communications | Fast Retailing vs. Tower Semiconductor | Fast Retailing vs. Taiwan Semiconductor Manufacturing |
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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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